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ateam-mcp

by io.github.ariekogan

ateam_get_chain

Inspect the full chain tree — the whole run rooted at chain_id, walking down through every handoff and askAnySkill subcall.

Use when a chain has already run and you want to analyze the structure: which skill called which, how deep the call tree went, which tool inside which job invoked which sub-tool. The two main shapes: • response.data.chainJobs[] — one entry per job in the chain. Fields: jobId, skill, status, iteration, depth (0 = root, +1 per askAnySkill subcall hop), relation ('root' | 'subcall' | 'handoff'), parentJobId, parentSkill, goal. • response.data.executionSteps[] — every tool call across all chain jobs, tagged with _skill, _jobId, _depth (= job depth), _relation, _parentSkill, _parentJobId, _toolDepth (tool-in-tool nesting via opId/parentOpId).

Differs from ateam_test_status by purpose: status is for live polling of a job you just kicked off; get_chain is for post-hoc tree analysis (debugging multi-skill flows, regression testing, comparing two runs).

Auth: forwards your authed api_key. Tenant scoped by the key itself. Actor scoping: you can only inspect chains rooted at jobs your actor has access to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNoAlias for chain_id. Any job inside the chain works — Core walks up to the root — but you rarely hold one; prefer chain_id.
actor_idNoOptional. WHO is asking. A job belongs to an actor and Core enforces that on per-job reads, so a tenant key alone is refused. Usually unnecessary — the session remembers the actor from ateam_conversation/ateam_test_skill. Pass it to inspect a job run by a DIFFERENT actor (e.g. a real user's).
chain_idNoTHE EXECUTION'S IDENTITY — what ateam_conversation returns and what you actually hold. A chain is the whole run: root job + every handoff + every askAnySkill subcall. Prefer this.
skill_slugNoOptional. The skill slug for the job — speeds up the lookup when the job isn't in memory and must be loaded from storage. Omit if you don't have it; lookup still works but does an extra round-trip.

TDQS

A4.9/5.0
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 covers auth/key forwarding, tenant scoping, and actor-level access restrictions. It also explains the two response shapes and the depth/relation semantics. This is substantial behavioral context beyond what the schema provides.

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 long but every part is functional: purpose, output shape, differentiation, and auth/scoping are all distinct. The bullets keep the two response shapes scannable and the content is front-loaded with the core purpose. It earns its length.

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?

No output schema exists, so the description compensates by enumerating chainJobs[] and executionSteps[] fields, including depth and relation semantics. It also covers auth and scoping constraints needed to call correctly. For a non-mutating inspection tool, nothing essential is missing.

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 descriptions cover all four parameters, so the baseline is 3. The tool description reinforces the preferred parameter (chain_id), explains why actor_id may be required, and notes the performance effect of omitting skill_slug. This adds modest extra context beyond the schema, though the parameter-specific load is mostly carried by the schema.

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 — 'Inspect the full chain tree' rooted at chain_id — and itemizes exactly what the tool returns (chainJobs[] and executionSteps[]). It also explicitly contrasts itself with ateam_test_status, so an agent can distinguish it from a close sibling.

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 states a precise trigger: 'Use when a chain has already run and you want to analyze the structure' and provides a direct alternative comparison: ateam_test_status is for live polling while get_chain is for post-hoc analysis. This is explicit when-to-use guidance.

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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