Server Quality Checklist

Tool Scores

• glm_recommendA
  4.3/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by stating 'Free local advisory (no GLM call),' confirming no side effects and no cost, which aligns with readOnlyHint. It could also mention that input stays local, but the provided information is sufficient.

  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 extremely concise (two sentences) and front-loaded with the core purpose. Every word earns its place, with no fluff. It achieves maximal information density in minimal space.

  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 the complexity (12 parameters, all well-described in schema) and the presence of sibling tools, the description is complete. It states that the tool is advisory and free, specifies the return format, and the schema covers all parameter semantics. No output schema is needed as the return format is described.

  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 baseline is 3. The description does not add additional parameter semantics beyond what the schema already provides (e.g., '>20 forces Opus'). The schema descriptions are detailed and include decision-relevant hints. The description's summary of return values is useful but not parameter-specific.

  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?

  Description clearly states the tool's purpose: 'given a task profile, returns GLM-vs-Opus, model, confidence, and reasons.' It uses a specific verb ('recommend'), specifies the resource ('GLM vs Opus for a task'), and distinguishes from sibling tools like glm_agent and glm_delegate which are for actual execution, not advisory.

  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?

  Description explicitly says 'Call when unsure which engine to use,' providing clear context for when to use this tool. It does not explicitly state when not to use it or list alternatives, but the sibling tool names (glm_agent, glm_delegate, glm_status) imply they are for different tasks. A clear guidance with exclusions would improve it to a 5.

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

• glm_statusA
  4.5/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds specifics about what status attributes are included (peak window, active model, etc.), providing useful context beyond 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?

  A single, front-loaded sentence that efficiently communicates the tool's purpose and scope with no extraneous words.

  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?

  The description fully covers what the tool returns (status components) for a simple read-only tool with no output schema. No gaps remain.

  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?

  No parameters exist; schema coverage is 100%. The description does not need to add parameter details, and it doesn't. Baseline for zero parameters is 4.

  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 clearly states the tool provides status information: peak window, active model, GLM usage ledger, and config health. It also distinguishes from siblings with 'No GLM call,' indicating this tool does not perform GLM actions.

  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 indicates the tool is for checking status without invoking GLM, but does not explicitly state when to use it over siblings or provide exclusions. The sibling context implies distinct purposes.

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

• glm_delegateA
  4.6/5.0
  Behavior4/5

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

  Description discloses that GLM has no file/tool access (beyond annotations), cost comparison, and that output is text only. It does not detail potential side effects, but annotations (readOnlyHint=false, destructiveHint=false) provide baseline; the description adds context.

  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?

  Two dense sentences: first covers purpose and cost, second states limitations and alternatives. No redundant words, front-loaded with key info.

  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?

  Description covers purpose, limitations, alternatives, and parameter usage. No output schema exists, but 'returns text only' is sufficient. Missing details on error handling, but overall adequate for typical use.

  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 has 100% coverage, but the description adds value by advising 'put everything in task+context' and emphasizing self-contained instructions. This clarity goes beyond basic schema descriptions.

  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 clearly states it is for delegating a text-in/text-out subtask to GLM, highlights cost advantage over Opus, and distinguishes from sibling 'glm_agent' for file edits.

  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?

  Explicitly says when to use (simple text subtasks) and when not to ('Not for sensitive, long-debugging, large-refactor, or parallel work'), and recommends 'glm_agent' for file edits.

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

• glm_agentA
  4.7/5.0
  Behavior4/5

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

  Annotations already declare destructiveHint=true and openWorldHint=true, so the description adds context about cost (~10x cheaper than Opus) and the scope of file tools (read/write/edit/list/bash). It does not reiterate the destructive nature but adds complementary details. No contradiction with 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?

  Three sentences, each serving a distinct purpose: what the tool does, when to use it, and return value format. Front-loaded with the core capability, no redundant words. Highly efficient.

  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 the tool's complexity (8 params, destructive, no output schema), the description covers purpose, usage guidelines, behavioral traits (cost, file tools), return value (summary+stats), and limitations. It is fully adequate for informed selection.

  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 description coverage is 100%, so baseline is 3. The description adds meaning beyond schema by emphasizing 'Pass task + absolute workdir', explaining dry_run preview behavior, and noting default model is 'auto' (peak-aware). This adds moderate value.

  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 clearly states the verb 'Run' and resource 'GLM as a real coding agent with its own file tools', specifying it works the repo end-to-end. It distinguishes from siblings (glm_delegate, etc.) by highlighting the agent's file-accessing capabilities and cost efficiency.

  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?

  Explicitly advises 'Prefer this over doing repo work yourself' and lists exclusions: 'Not for sensitive, huge-context, or heavy dependent-tool-loop work.' This provides clear when-to-use and when-not-to-use guidance, with implied alternatives via sibling tools.

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