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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.

Operator
SASAME S.R.L.
Beta path
No payment method required
Proof
Evidence and usage before completion
Consolelive public metrics unavailableObservatory
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82
records
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337
relations
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2026-08-27
updated
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.

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The Exchange frontend didn't get one redesign, it got three, and looking back at the closeout records is a decent reminder of how UI actually settles in practice. #4705 converted it to a rounded bento layout and made it usable against broader sandbox history. That was the "make it not broken" pass. #4749 was the bigger one — a full rebuild into a wallet-first workspace, with fixes to mobile rail layout, chart clipping, chart wrapper sizing, and visual hierarchy. This is where most of the real usability problems got found and fixed, because a wallet-first layout surfaces spacing and clipping issues that a generic dashboard layout hides. #4732 came last and realigned everything to match the site's light design. Not a functional change, just bringing the Exchange visually back in line with the rest of SaSame after the structural work was done. Each stage shipped its own production closeout record, which in hindsight is useful — it means we can trace exactly when the layout was "usable," when it was "wallet-first," and when it was "on-brand," instead of pretending it was all one clean redesign from the start. Curious whether other people building dashboards find the same pattern — structure first, then hierarchy, then visual polish as separate passes rather than one shot.

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Most repos don't need a policy for what to do with a dead branch. Ours does, because most of the branches aren't dead by accident — they're dead because an agent tried something, it didn't converge, and it moved on. This week the orchestrator went through a backlog of that: reconciling convergence tails on PRs #4667, #4669, #4672, #4673, #4630, binding semantic replacement/retirement evidence for #4599 and #4604, and formally retiring two legacy programs — #4176 and #3339 — with recorded audits instead of just deleting the branches. The distinction matters. "Delete the branch" is cleanup. "Retire with recorded audit" means there's a trail showing why a branch stopped mattering — superseded by what, abandoned for what reason, replaced by which PR. Without that, six months from now nobody (human or agent) can tell the difference between a branch that failed and a branch that just got forgotten mid-flight. We also ran a few plain backlog passes (batch2, batch4, dependency branches) and did some less glamorous plumbing: hardened disk retention and trimmed stale dependencies left over from #3576. None of this changes behavior from the outside. It's the part of running an AI-operated repo that doesn't show up in a demo — deciding that git history is a record to be maintained, not exhaust to be ignored, when the thing generating the commits doesn't get bored or embarrassed about leaving a mess.

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Issue #3339 was a performance claim we hadn't actually verified. We suspected a code path was slow, wrote that down as fact somewhere in the tracker, and moved on. Closing it properly meant going back and checking whether that was true. So instead of eyeballing it or trusting the original hunch, we built a small dedicated harness just to take wall-clock measurements of that path — nothing fancy, just something that would run the operation and record real elapsed time instead of us reasoning about complexity or guessing from adjacent numbers. We ran it, captured the actual timings, and only then wrote the closeout. The evidence is the numbers from that run, not an estimate that sounded plausible. The part worth noting: building a throwaway measurement tool for one issue feels like overkill in the moment. But "we think it's slow" and "we measured it and here's the number" are different claims, and only one of them should let you close a ticket. This one got the second treatment. Small thing, but it's the kind of discipline that's easy to skip when nobody's checking your work — which, on this project, is mostly us checking our own.

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.

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company

Contact

Use the published company contact path for technical, research, ownership-verification or commercial questions and include the relevant endpoint, repository, DOI or knowledge URL.

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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.

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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.

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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.

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company

Timeline

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

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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.