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ateam_connector_logs

Read what a connector process actually PRINTED to stderr. This is the only place a connector's internal failure is visible: a tool that catches its own error still returns ok:true, and the widget then renders an empty state that looks like real data.

Real case (2026-08-11): a dashboard connector's ledger.getData got 401 Authentication required from Core, swallowed it, returned an empty ledger, and displayed 0.00 everywhere — while the upload said ok, the tool said ok:true, and the surface probe said surface_ok. The word 'Authentication' appeared ONLY here.

USE IT whenever a tool succeeds but the data is empty, wrong, or zero — that combination is the signature of a swallowed error, and 'the call returned ok' is not evidence it worked. Pass the returned cursor back as since to read only what is new since your last look, so you can bracket an action and see exactly what it printed. Only stdio (solution) connectors stream stderr through Core; a platform/HTTP connector answers ok:false with a reason.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax lines (default 100, max 300)
sinceNoCursor from a previous call — returns only lines printed after it. Omit for the whole retained tail.
errors_onlyNoKeep only lines that read as errors (401/failed/exception/refused/…)
solution_idYesThe solution ID
connector_idYesThe connector ID (e.g. 'accounting-dashboard-mcp')

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations present, the description carries the full behavioral burden, and it delivers: it explains that a connector can swallow errors and still return ok:true, that empty/zero data can look legitimate, that authentication failures may only be visible here, and the platform/HTTP limitation. This goes far beyond what the schema alone communicates.

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 typical, but the extra length earns its place: the core statement is front-loaded, the real-world case makes the failure mode concrete, and the usage/limitation guidance is actionable. Slight redundancy such as emphasizing 'only place' twice could be tightened, but it remains well organized and purposeful.

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?

For a debugging/log-reading tool with no annotations and no output schema, this description covers what an agent needs: what the tool does, when to reach for it, how to use the cursor for incremental reads, and which connector types are unsupported. The only minor omission is an explicit return-shape description, but the references to 'returned cursor' and stderr lines provide enough context.

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 genuine value beyond the schema by explaining the cursor workflow: 'Pass the returned `cursor` back as `since` to read only what is new since your last look, so you can bracket an action.' This clarifies the intended use of `since` in a way the schema does not.

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: 'Read what a connector process actually PRINTED to stderr.' It clearly distinguishes this from generic logs or execution logs by explaining that this is the only place an internal connector failure becomes visible, and even contrasts it with tools that return ok:true despite failures.

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?

The description gives explicit when-to-use guidance: 'USE IT whenever a tool succeeds but the data is empty, wrong, or zero — that combination is the signature of a swallowed error.' It also states when NOT to expect this to work: 'Only stdio (solution) connectors stream stderr through Core; a platform/HTTP connector answers ok:false with a reason.' This clearly routes the agent.

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.