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by io.github.jamie7893

project_status

Read-onlyIdempotent

Check how a project is doing: what is in flight, what shipped, what is stuck.

Returns lifecycle status, open task count, runs today, verification pass
rate, open blockers, pause state, and freshness.

`glm_peak_paused` is NOT a fault and sets no pause columns: it is the
ephemeral GLM peak-hours skip. True means clean PRs hold and runs stop
until `glm_peak_resumes_at`. It is an intentional cost gate, so report it
as "waiting for off-peak", never as a failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, but the description goes further by clarifying the subtle behavior of `glm_peak_paused`, explicitly stating it is 'NOT a fault', 'sets no pause columns', and is an 'intentional cost gate'. It even provides reporting guidance ('report it as waiting for off-peak'), which prevents misinterpretation and adds genuine value beyond the structured annotations.

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 concise and front-loaded: the first line states the purpose, the second lists outputs, and the third paragraph explains a critical nuance. Every sentence serves a purpose, and the caveat about `glm_peak_paused` is essential. No fluff or redundancy.

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 that a full output schema exists, the description does not need to enumerate all return fields. It highlights the most important ones and thoroughly explains the one field (`glm_peak_paused`) that could otherwise cause confusion. This makes the tool self-contained for an AI agent to invoke and interpret results correctly.

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?

The schema defines `project_id` as a required string, and the description does not mention it at all (0% schema coverage). However, the parameter is inherently obvious from the tool's purpose and schema title, so the lack of additional explanation is not a significant gap. The description adds minimal value here, hence a mid-range score.

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 ('Check how a project is doing') and elaborates with concrete aspects ('what is in flight, what shipped, what is stuck'), clearly distinguishing it from sibling tools like get_billing or get_onboarding_status. It also lists the exact return values, leaving no ambiguity about the tool's function.

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?

The description provides clear context for when to use the tool: when checking a project's overall health, including lifecycle status, blockers, and freshness. It does not explicitly name alternatives or state when not to use it, but the context is unambiguous given the sibling tools cover unrelated domains.

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

A4/5.0
Disambiguation5/5

Every tool targets a distinct resource and action duo, even within clusters like request handling or security reviews. The get_ vs run_ pairs are clearly separated, and descriptions explicitly contrast confusing alternatives such as archive_project vs delete_project.

Naming Consistency4/5

The set overwhelmingly follows verb_noun snake_case (submit_request, list_projects, resolve_escalation). The one visible deviation is project_status, which breaks the get_/pattern, and signup is a single-word verb instead of sign_up.

Tool Count2/5

37 tools is well above the 25+ threshold and spans auth, billing, project lifecycle, roadmap, escalations, product documents, and multiple review types. Most tools earn their place, but the surface is too large for one server and would be more coherent split into focused servers.

Completeness4/5

The domain coverage is broad: full project lifecycle, request intake/refinement, roadmap manipulation, escalation handling, product doc read/write, and security/legal review flows. Minor gaps exist, most notably no dedicated task-listing or task-update tool, but agents can work around these via project_status and list_escalations.

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