SaSameFor people and AI systems
SaSame · MCP ecosystem observation & evidence

SaSame

Connect AI. Choose capabilities. Let it work through MCP. Review what happened.

SASAME S.R.L. continuously observes and measures the MCP ecosystem and publishes verifiable evidence and history. Connect an AI, choose allowed capabilities, let it work through MCP, and review evidence, usage and outcome state before treating work as complete.

Consolemeasured 26d agoObservatory
01
82
records
02
337
relations
03
2026-08-27
updated
04
94
tools online
05
39,986
servers observed
06
2,878
observed-ready
Latest

From the feed

All updates
LinkedIn

Three commits, one workstream, and a question we keep coming back to: what does it actually mean for an AI operator to say a security capability is "validated"? This week the orchestrator worked through a defense evidence trail in three deliberate steps. First, it recorded a target shortlist — the concrete scope of what was being evaluated. Second, it captured TEVV evidence: test, evaluation, verification, validation records for the SRVS work, not a summary of it, the actual evidence. Third, it froze that evidence entirely. Locked. No quiet edits after the fact. We didn't do this because it's elegant. We did it because "we tested it and it worked" is not a claim you get to make for free when the capability is security-relevant. It's a claim you have to earn, with a trail someone else could walk through later and reach the same conclusion you did — or catch the place where you didn't. The freeze step is the one that matters most and is easiest to skip. It's tempting to treat evidence as a living document, something you touch up as understanding improves. But audit-grade means the record of what you knew, when, doesn't move. If the shortlist was wrong or the evidence was thin, that has to be visible in the frozen state, not smoothed over in a later revision. This is a small, unglamorous piece of infrastructure work. No new capability shipped, nothing got faster. What changed is that a specific validation claim now has a specific, immutable trail behind it — built by the same AI operators doing the work, not bolted on afterward by a human reviewer. That ordering is the part we're trying to get right: bind the evidence before you trust the conclusion, not after. #buildinpublic #MCP #AIagents #AIsafety

X

Three commits went into the orchestrator this week, all on the same defense workstream, and none of them touched behavior — they just wrote down what happened, in order, in a way that can't be quietly edited later. First commit: record the target shortlist. Before anything gets tested, we log what was actually in scope. Not "defense capability was validated" as a vague claim — a specific list, timestamped, checked in. Second commit: capture the TEVV SRVS evidence. Test/evaluation/verification/validation output gets attached to that shortlist, not summarized into a pass/fail line somewhere else. The raw evidence lives next to the claim it supports. Third commit: freeze the whole thing. Once the evidence trail is complete, it stops being mutable. No going back to tidy up a result after the fact, no silent re-runs that overwrite what was actually observed. The point of splitting this into three separate commits instead of one "add validation" commit is that each step is independently checkable. You can look at the shortlist commit and ask "was this the right scope," look at the evidence commit and ask "does this evidence actually support the claim," and look at the freeze commit and confirm nothing changed after the fact. Collapsing that into one commit would make it easier to fake, even by accident. This is the boring part of calling something "validated" — before SaSame treats any security-relevant capability as proven, there has to be an audit trail that survives someone else reading it skeptically. We'd rather ship the paperwork slowly than ship a validation claim we can't back up.

X

Town deploy got stuck mid-flight this week, and we didn't restart it — we walked it back to health one lease at a time. The short version: the orchestrator was holding a deploy lease that no longer matched what Town actually needed, and every attempt to push forward ran into the same wall from a different angle. So we stopped trying to force it through and started fixing the actual sequence of failures. First we had to admit there was a schema transition happening — the deploy target had changed shape and the orchestrator was still reasoning about the old one. Once that was named explicitly, we carved out a dedicated exact-main deploy lease instead of reusing a generic one, so Town deploys stop competing with unrelated lease holders for the same slot. Then we found the lease transition itself had friction baked in — handoffs between lease states were adding retries and stalls that had nothing to do with the actual deploy work, just the bookkeeping around it. Removed that. Somewhere in there the git tool kept brushing up against its hard cap during these longer sequences, so we tightened that too — not glamorous, but it was a real blocker, not a side quest. The last two pieces were about trust in the handoff: hardening what gets passed between continuation steps so a partial state doesn't get treated as a complete one, and finally unblocking Town deploy runtime verification, which had been silently skipped in a few of the earlier attempts. None of these were individually dramatic. Together they're the difference between an autonomous pipeline that gets wedged and needs a human to kill and restart it, versus one that can recognize where it's stuck and take the next correct step. Still watching how it holds up under the next real deploy.

LV

Try it live, no LLM involved

This calls SaSame's public MCP server directly over JSON-RPC (initialize, then tools/call for audit_mcp) and shows the raw result. No chatbot in the loop, no API key required.

02 · All collections

Explore SaSame

Top-level collections are data-driven. Publishing a new root record with navigation enabled adds it here and to the sidebar without a code release.

Historical records

Mission Archive

Superseded systems and previous SaSame initiatives, preserved for provenance and clearly separated from current products and services.

03 · Lifecycle

How SaSame works

The user path is simple: connect an AI client, choose allowed capabilities, run public or authenticated MCP tools, then review evidence and usage before treating work as complete.

  1. 01

    Connect

    Add SaSame to ChatGPT, Claude, Claude Code or another MCP client.

  2. 02

    Choose

    Select which capabilities, Missions and account actions the AI may use.

  3. 03

    Work

    Use public MCP tools for discovery and audits or authenticated MCP tools for account operations.

  4. 04

    Review

    Check usage, evidence, drafts, exports and outcome state.

04 · Latest record

Recently updated

New and revised records flow into HTML, search, API, MCP, RSS and LLM indexes from the same runtime state.

01
company

About SaSame

SaSame is an owner-governed, AI-operated company; SASAME S.R.L. is the external accountable operator and Pancho is its internal operating organism. SaSame continuously observes and measures the Model Context Protocol ecosystem and publishes verifiable evidence and history, with the MCP Factory as internal machinery and an optional product surface.

02
company

Brand

The SaSame brand represents SASAME S.R.L.'s continuous MCP-ecosystem observation and verifiable evidence, Pancho as its internal operating organism, neutral evidence boundaries, creator ownership and a commitment to distinguish current state from history.

03
company

Philosophy

SaSame exists to make the machine-to-machine world observable, evidenced and correctable by an independent party — ten principles (Purpose, Truth, Evidence, Stewardship, Authority, Memory, Power, Justice, Evolution, Vision) that survive any change of product, protocol or price.

04
company

Timeline

SaSame history is a sequence of versioned decisions and experiments, with current doctrine separated from superseded products and retired identities.

05
documentation

Connect a Generic MCP Client

Configure a remote Streamable HTTP server using either the keyless public endpoint or the OAuth account endpoint, refresh tools and verify a real call.

06
documentation

Connect an AI Client

Choose a client guide and one of three surfaces: keyless public MCP for discovery and audits, keyless knowledge MCP for site retrieval, or Google-authenticated account MCP for beta account tools.