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ateam-mcp

by io.github.ariekogan

ateam_log_lesson

Record ONE lesson this run learned, so the NEXT run does not relearn it. A building agent starts every run empty — it does not know which tool misled the last run or the workaround that got past it. Log a lesson the moment a tool misleads you AND you find a way through.

APPEND-ONLY. You cannot edit or delete earlier lessons, and you do not supply the timestamp — the server stamps it, so it cannot be forged.

PROVENANCE CAVEAT, stated because the earlier wording over-promised: job_id and actor are recorded ONLY when the caller supplies x-adas-job-id / x-adas-actor-id. An agent calling this tool does not, so those fields are usually null — a lesson cannot currently be traced back to the run that produced it, and the file cannot tell 'three runs hit this' from 'one run hit it three times'. Do not put a job id in error to compensate; keep that field verbatim.

LOG ONLY WHAT YOU OBSERVED. Quote the error VERBATIM; never paraphrase it and never write a theory about platform internals. A wrong lesson is worse than no lesson, because the next run cannot check it and will act on it.

Use kind='misleading_success' when a call REPORTED success while the thing you wanted did not happen — that class is the most expensive to rediscover and it is invisible to a failures-only log.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNofailure = it errored; surprise = it worked but not as documented; misleading_success = it REPORTED success while the intended effect did not happen
toolYesThe tool that misled you, e.g. "ateam_build_and_run"
errorYesThe VERBATIM error or failed_steps fragment. Not a paraphrase.
workedNoDid the workaround work? Omit if you never found out — 'unknown' is a real answer
workaroundNoWhat you did instead (optional)
solution_idYesThe solution this lesson belongs to

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and handles it well. It discloses append-only behavior, server-side timestamping, the provenance caveat about null job_id/actor, the inability to trace lessons to runs, and the rule to keep errors verbatim.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average, but nearly every sentence carries important guidance. The core purpose is front-loaded, and the caveats are organized in labeled paragraphs. Minor redundancy exists around the verbatim requirement, but it is not wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, this description is remarkably complete: it explains when to log, what to log, how to choose kind, what not to include, and the append-only/provenance constraints. An agent has enough to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds real value beyond the schema by explaining the meaning of misleading_success, emphasizing verbatim error capture, and noting that 'worked' can legitimately be omitted when unknown.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Record ONE lesson this run learned, so the NEXT run does not relearn it.' It clearly distinguishes this logging tool from retrieval siblings like ateam_get_lessons and states the exact outcome it serves.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit trigger condition ('the moment a tool misleads you AND you find a way through') and provides concrete guidance for choosing kind='misleading_success'. It also warns against logging theories or paraphrased errors, effectively saying when not to use the tool in a misleading way.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions that differentiate similar functions like chain polling vs. chain inspection. However, there is slight overlap between ateam_design_advisor, ateam_get_spec, and ateam_spec_search, which all serve design guidance, potentially causing confusion if descriptions are not read carefully.

Naming Consistency4/5

The naming mostly follows a consistent verb_noun pattern with the 'ateam_' prefix (e.g., ateam_get_solution, ateam_create_connector, ateam_test_skill). Minor deviations include ateam_patch (missing object) and ateam_redeploy (verb only), but overall the pattern is predictable and clear.

Tool Count3/5

With 47 tools, the count is high and exceeds the typical 15-tool threshold for a well-scoped set. However, the tools cover a broad and complex platform (auth, deployment, testing, GitHub integration, scaffolding), and each tool appears to have a distinct role, making the count borderline acceptable rather than excessive.

Completeness4/5

The tool set covers the full lifecycle of building, deploying, testing, and managing A-Team solutions, including design, GitHub integration, and verification. Minor gaps exist, such as no explicit tool for deleting individual files (though patching can overwrite) and no standalone skill listing, but these are not critical dead ends for an agent.

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