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ateam-mcp

by io.github.ariekogan

ateam_get_execution_logs

Get execution logs for a solution — recent jobs with step traces, tool calls, errors, and timing. Essential for debugging what actually happened during skill execution. (Advanced.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax jobs to return (default: 10, max: 50)
job_idNoOptional: get detailed trace for a specific job ID
actor_idNoThe actor whose job this is. REQUIRED for per-job detail: a job belongs to an actor and Core refuses the detail endpoint without one (the list form does not check). Use the same actor_id you passed to ateam_conversation.
chain_idNoThe CHAIN id — what ateam_conversation returns and ateam_chain_status takes. Prefer this: it is the id you actually hold. Resolved to the underlying job for you.
skill_idNoOptional: filter logs to a specific skill
solution_idYesThe solution ID

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral disclosure burden. It discloses the shape of returned data (recent jobs with traces, tool calls, errors, timing) and implies a read operation through 'Get', but does not mention auth needs, side-effect absence, or edge-case behavior beyond what the schema separately documents.

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

Conciseness5/5

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

The description is two functional sentences plus a succinct '(Advanced.)' signal. The core purpose is front-loaded, and every clause adds either purpose, content, or usage context without waste.

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

Completeness4/5

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

For a 6-parameter read tool with no output schema and no annotations, the description plus the rich schema explanations give an agent what it needs to invoke the tool. It could be more complete about the expected return shape and how to obtain a job_id, but the stated contents and detailed parameter docs make it sufficient in practice.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all six parameters, including the important actor_id requirement and chain_id preference. The main description adds no parameter-level meaning, so the high-coverage baseline of 3 applies.

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

Purpose4/5

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

The first sentence states a clear verb and resource: 'Get execution logs for a solution' and enumerates the log contents ('step traces, tool calls, errors, and timing'). This is unambiguous, though it does not explicitly contrast with sibling logging/status tools such as ateam_chain_status or ateam_connector_logs.

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

Usage Guidelines4/5

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

'Essential for debugging what actually happened during skill execution' provides a clear use case. It does not specify when to prefer a sibling tool or list exclusions, but the debugging context is enough to guide selection.

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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