qiaomu-llm-mcp

by joeseesun

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clear, distinct purpose. For example, qllm_chat handles single provider calls, qllm_compare compares multiple providers, and qllm_heavy_discuss facilitates multi-perspective discussions. The Claude Code tools are specialized and separate from the general chat and management tools.
Naming Consistency5/5
All tools follow a consistent 'qllm_' prefix and snake_case naming convention. They use a verb_noun pattern (e.g., list_models, show_provider, claude_code_run) that makes their function predictable and easy to parse.
Tool Count5/5
With 13 tools, the set is well-scoped. It covers chat, comparison, heavy discussion, pipelines, routing, provider management, and Claude Code integration without being excessive or sparse. Each tool addresses a specific need within the domain.
Completeness5/5
The tool set comprehensively covers the core operations of a local LLM registry: listing providers and models, calling models in various configurations (single, compare, heavy discuss, pipeline), routing, and Claude Code integration. No obvious gaps are present for the stated purpose.
Average 3.9/5 across 13 of 13 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on June 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false, destructiveHint=false, and openWorldHint=true. The description adds no further behavioral context (e.g., side effects, rate limits, auth requirements), leaving the agent to infer behavior solely from 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 extremely concise: one sentence for purpose, then a brief Args/Returns structure. Every part earns its place, with zero waste and clear front-loading.
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?
The tool is complex (many parameters, nested ChatInput), but the schema fully documents inputs and the description notes the return format. Missing details about routing or task_type behavior are covered by schema. The combination is nearly complete.
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?
The schema provides detailed descriptions for all parameters, so the description's high-level categorization of parameter groups adds convenient clarification. With full schema coverage, the description offers useful summarization without being redundant.
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 description clearly states it calls a provider from the local registry for chat, using specific verb and resource. However, it does not explicitly differentiate from sibling tools like qllm_pipeline or qllm_claude_code_run, so a slight deduction applies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as qllm_pipeline or qllm_compare. The description only explains what it does, not when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate not read-only, not destructive, not idempotent. Description adds context about writing to disk (report_base_dir defaults to ~/Downloads/heavyskill-reports), which is useful 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: one for purpose, one for args and returns. Concise but the args line is a bit dense; could be split for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having a complex input schema with nested objects, the description is thin. It does not explain error handling, file output behavior, or the relationship between traces and synthesis. Output schema exists but description doesn't clarify return structure.
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 0%. Description provides a high-level summary of the params (Question, mode, traces, synthesis, etc.) but lacks detailed semantics for each field.
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 renders a Codex-hosted HeavySkill synthesis to Markdown and HTML. The verb 'render' and resource are specific, and the tool is distinct from siblings like qllm_heavy_discuss or qllm_chat.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, when-not to use, or comparison with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe operation. The description adds valuable context: 'without exposing API keys' and 'with secret status only,' which is critical for security and beyond what annotations provide.
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 concise with a clear first sentence and a structured list of arguments and returns. Every part is relevant, and there is no superfluous text.
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?
Given the simple nature of the tool (listing providers with filters), annotations cover safety, and output schema exists, the description is sufficiently complete. It explains the return format and security aspect, though it could briefly mention the 'local' scope.
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 description lists the parameters and their meanings, but the input schema already contains descriptions for each property. The enum for response_format is clear from the schema. The description adds value by summarizing, but does not introduce new information beyond the schema.
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 description clearly states the tool's purpose: 'List local qiaomu-llm providers without exposing API keys.' It specifies the resource and the important security caveat, though it does not explicitly differentiate from sibling tools like qllm_show_provider.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or specific scenarios. The agent is left without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read operation. The description adds value by specifying exactly what is inspected (version, wrapper capabilities, help text) and the return format (JSON or Markdown), providing behavioral 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?
The description is concise: one sentence for purpose, followed by structured Args and Returns sections. It is front-loaded and contains no superfluous text, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description conveys the basic operation and return type (string), but lacks details about the returned JSON structure or keys, especially since there is no output schema provided. It is adequate but could be more exhaustive given the complexity of the tool.
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 description summarizes the parameter group ('Optional executable path, help inclusion, timeout, and response format') but does not add meaning beyond the input schema, which already contains detailed descriptions for each property. Because schema description coverage is effectively high (per the schema), the description offers marginal added value, achieving the baseline of 3.
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 'Inspect' and the resource 'local Claude Code CLI version, wrapper capabilities, and optional help text'. This distinguishes it from sibling tools like qllm_claude_code_run (execution) and qllm_claude_code_models (listing models), making the tool's purpose specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used for inspecting capabilities but does not explicitly state when to use it over alternatives, nor does it provide exclusions or prerequisites. Usage context is only implied, not actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds that it returns JSON or Markdown and notes that exact account availability can vary, providing useful behavioral 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and follows a clear docstring structure with Args and Returns sections. It is front-loaded with the main purpose. Minor room for improvement in explaining parameter details.
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?
Given the tool has one complex parameter and an output schema, the description provides enough context for an agent to understand the tool's purpose and return format. It notes variability in account availability.
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 description mentions 'Catalog/session inclusion options' which adds some context, but schema description coverage is 0% and property descriptions in the schema are detailed. The description does not fully compensate for the lack of top-level param description.
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 lists Claude Code model aliases, docs-derived model ids, and recent local usage. This distinguishes it from sibling tools like qllm_list_models which list all models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for Claude Code-specific model info but does not explicitly state when to use this tool over alternatives or provide exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds valuable context: 'Raw secrets are not read from settings,' which goes beyond the annotations and clarifies security behavior. No contradictions.
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 two sentences plus an Args/Returns block. It is front-loaded with the purpose and efficient, with no extraneous information. The Args/Returns block is slightly redundant given the schema but does not detract.
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?
Given the tool has one parameter with clear sub-properties, annotations, and an output schema, the description covers the essential use case and mentions security ('Raw secrets are not read from settings'). It does not explain session structure, but that is reasonable domain context.
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 description lists parameters briefly: 'Cwd, limit, prompt inclusion, and output format.' However, the input schema already provides detailed descriptions for each sub-property (e.g., cwd's description, limit's min/max). The description adds little meaningful context beyond 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 clearly states the tool inspects recent local Claude Code JSONL sessions for models and prompts. This is a specific verb+resource that distinguishes it from siblings like qllm_claude_code_run (execution) and qllm_list_models (listing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (to inspect sessions) but does not explicitly contrast with siblings or provide when-not or alternative tool references. The sibling list is provided externally but not leveraged in the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-destructive, non-read-only, open world. Description adds that the tool may write reports and HTML files (if write_report set), and that host_mode can return a prompt instead of running (codex mode). These are important behavioral details beyond the annotations.
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 a single paragraph that covers purpose, args, and returns. It is concise and front-loaded with the main action. Could be slightly more structured but not overly verbose.
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?
Given the tool's complexity with many parameters and nested schema, the description adequately explains the overall concept and returns. It mentions key options like host mode and report writing. The output schema exists to detail return values. Minor gap: does not explain the two host modes in detail.
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 input schema (HeavyDiscussInput) contains detailed descriptions for all parameters. The description provides a high-level summary but adds little new meaning beyond what the schema already offers. The summary is useful but not essential.
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 runs a HeavySkill-style multi-perspective discussion across providers. It specifies the action (run), method (multi-perspective discussion), and scope (across providers). This distinguishes it from siblings like qllm_chat or qllm_compare.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives. Usage is implied by the complexity of the discussion, but no direct guidance on when not to use or conditions for choosing over siblings like qllm_pipeline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds 'without calling a model' and return type, consistent with annotations. No contradiction.
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?
Description is concise with one sentence plus structured Args/Returns. No fluff, but could front-load purpose more efficiently.
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?
Given complex input schema and presence of output schema, description adequately covers purpose, input high-level, and return type. Schema fills remaining details.
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 coverage is 0% from description, but the schema itself provides full descriptions for all parameters. Description only gives a high-level summary, adding little beyond schema. Baseline 3 applies.
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 ranks providers for a task without calling a model, using specific verb and resource. It distinguishes from siblings like qllm_chat which actually calls a model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for ranking providers without making a call, but does not explicitly state when to use this versus alternatives like qllm_chat or qllm_list_models. No when-not or alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only, non-destructive, non-idempotent, and open world. The description adds 'non-interactive' and details about stdin, stdout/stderr, but does not elaborate on potential side effects despite openWorldHint being true.
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: two sentences with a parameter list. It is front-loaded and every sentence serves a purpose without redundancy.
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?
Given the tool's complexity (many parameters, output schema, annotations), the description covers the essential purpose, input, and output. It does not detail every parameter but the schema compensates. Suitable for a tool with rich structured metadata.
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?
The description groups parameters (Prompt, cwd, model/effort, etc.) and notes that prompt is passed via stdin, adding context beyond the schema. However, the schema already contains descriptions for most parameters, so the added value is moderate.
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 runs a non-interactive Claude Code CLI task, distinguishing it from sibling tools like qllm_chat or qllm_claude_code_sessions. The verb 'run' and resource 'Claude Code CLI' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for non-interactive code tasks but does not explicitly discuss when to use this tool versus alternatives like qllm_chat or qllm_heavy_discuss. No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true. The description adds valuable context: that remote mode calls the provider's /models endpoint, and output can be JSON or Markdown. This exceeds minimal expectations.
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: a single-line summary followed by an Args block. Every sentence adds value with no redundancy or clutter.
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?
Given the tool's moderate complexity (one nested parameter) and the presence of output schema and annotations, the description covers key behaviors (source modes, endpoint call) but could mention how 'auto' source resolves.
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 description restates parameter names (provider id, source mode, timeout, output format) without adding semantics beyond what the schema already provides. Schema coverage per context is 0%, but schema embedded descriptions actually exist, making this a baseline 3.
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 'List configured or remote models for one provider,' specifying the verb (list), resource (models), and scope (per provider). This distinguishes it from siblings like qllm_list_providers and qllm_show_provider.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for listing models but provides no explicit guidance on when to choose this tool over alternatives, nor any conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and openWorldHint=true, implying external API calls and potential side effects. The description mentions it runs a pipeline and returns a trace, but does not disclose costs, latency, or resource consumption. The timeout parameter in schema is noted but not in description. With annotations carrying part of the burden, a 3 is appropriate.
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 with two short paragraphs, front-loading the main purpose. Every sentence provides relevant information without redundancy. The structure is clean and easy to parse.
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?
The description covers purpose, parameters, and return format. Given the complexity of a multi-step pipeline and the presence of annotations and output schema, the description is reasonably complete. It lacks details on error handling or execution guarantees, but these are not critical for a tool definition.
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?
The description summarizes the PipelineInput as 'Initial input and 1-8 steps' and explains step autonomy and chaining. Although the input schema has individual descriptions, the description adds high-level context and structure. Given schema coverage 0% in description, it does add meaning, but the schema is rich so baseline is 3, and the extra chaining detail justifies a 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 'Run a sequential multi-model pipeline', which specifies the verb, resource, and structure. It distinguishes from sibling tools like qllm_chat (single interaction) and qllm_route_task (task routing) by emphasizing the multi-step sequential nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit when-to-use or when-not-to-use guidance. It implies usage for multi-model pipelines, but does not compare to alternatives or state prerequisites. The context of siblings suggests differentiation, but the description itself lacks such guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that failures are returned per provider and output format (JSON or Markdown), adding value beyond annotations which only set openWorldHint. No contradictions 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?
Extremely concise: two sentences for purpose and a clear Args/Returns structure. No unnecessary words, every sentence earns its place.
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?
Covers key aspects: multiple providers, same prompt, per-provider failures, and return format. Could add default behavior (e.g., 'defaults to router top providers') but overall adequate given complex input schema.
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 description adds comparative context ('Same fields as qllm_chat plus providers/max_providers'), but the schema already provides detailed descriptions for all CompareInput fields, so the description adds limited new meaning.
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 states a specific verb ('Call multiple providers') and resource ('same prompt') with clear result ('return comparable results'). It distinguishes from siblings like qllm_chat by emphasizing multi-provider comparison.
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?
References qllm_chat as a baseline ('Same fields as qllm_chat plus providers/max_providers'), implying use for comparing providers instead of single provider calls. However, no explicit when-not-to-use or alternative scenarios are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and idempotent, and the description adds critical safety context: 'Raw secrets are never returned'. This goes beyond annotations and builds trust.
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 brief (four lines), front-loads the key purpose, and uses a clear Args/Returns structure without any fluff.
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 simple read-only tool with strong annotations, the description adds the essential safety note and clarifies the return type. It could be slightly more detailed about metadata structure, but overall sufficient.
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 description merely restates the parameter names and types ('Provider id and output format') with no new meaning beyond the schema's descriptions, which already cover the parameters adequately.
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 'Show one provider's non-secret metadata and secret status', using a specific verb and resource, and differentiates from siblings like qllm_list_providers which likely lists all providers.
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 implies use for accessing details of a specific provider, and the name contrasts with listing tools, but it does not explicitly state when not to use or mention alternatives.
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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