@aiorouter/mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.1.1
Disambiguation5/5
Each tool targets a distinct resource or action: chat, model listing, model details, comparison, pricing, cost estimation, usage, connection test, config export, and presets. While list_models and get_model_info both relate to models, the former enumerates models while the latter provides detailed info for a single model, making the distinction clear. The descriptions reinforce these boundaries.
Naming Consistency5/5
All tools follow a consistent aiorouter_ prefix with a verb_noun pattern: chat, list_models, get_usage, get_pricing, export_config, compare_models, etc. Naming is uniform, snake_case, and verbs align with the operation. No mixed conventions or vague verbs like 'do_thing'.
Tool Count5/5
With 10 tools, the server is well-scoped for an AI router service. Each tool earns its place by covering distinct user needs: interaction, exploration, cost management, and configuration. This is within the ideal 3-15 range and neither feels sparse nor overwhelming.
Completeness5/5
The surface covers the core domain comprehensively: chat completions, model discovery (list, get, compare), pricing and cost estimation, account usage, connection validation, and configuration export. No obvious dead ends or missing essential operations for the stated purpose of interacting with AIOrouter. The only noted limitation (non-streaming) is a technical constraint, not a tool gap.
Average 4.1/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- No stable releases found
- 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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already declares this as a safe read operation, so the description does not need to restate that. However, the description adds no additional behavioral context beyond what the annotation provides, such as pagination behavior or response format. It neither contradicts nor enriches the annotation, so a mid-range score 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 two short sentences that front-load the core purpose and then add the filtering capability. There is no fluff or redundant elaboration. Every word earns its place, making it highly concise and readable.
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 list tool with one optional parameter and no output schema, the description covers the essential functionality. It could have briefly mentioned that this returns a full catalog or suggested when to use get_model_info for specific details, but the tool is simple enough that the current description is mostly complete. A slight gap is the lack of any information about the response structure, though this is not critical for a listing 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 input schema has 100% coverage for the single optional 'provider' parameter, including examples. The description merely repeats 'Filter by provider name' without adding new semantic details, so it does not surpass the schema's information. Baseline 3 applies because the schema already fully documents the parameter.
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 'List' and the resource 'all available AI models through AIOrouter', with an optional 'filter by provider name'. This distinguishes it from sibling tools like get_model_info (which targets a specific model) and compare_models (which compares), making the purpose 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 usage for listing models, but it does not explicitly state when to use this tool versus alternatives such as get_model_info or compare_models. There is no mention of exclusions or when not to use it, so the guidance is only implicit through the tool's name and purpose.
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 the description adds 'Uses public retail pricing' which clarifies the data source, and the word 'Estimate' sets expectations about accuracy. This adds useful behavioral context beyond the annotation, though it doesn't mention potential error conditions or rate limits.
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 a single sentence with no fluff, front-loading the primary action and key inputs. It earns its place without unnecessary detail.
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 estimation tool with three parameters and no output schema, the description adequately covers what the tool does and the pricing source. It lacks explicit return format but is sufficient for an agent to invoke correctly in most contexts.
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 100%, with clear descriptions for model and token parameters. The description only restates the parameter names without adding semantic detail, so the baseline score of 3 is appropriate.
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 estimates cost in USD for a prompt based on model and token counts, which is a specific action and resource. It distinguishes from sibling tools like get_pricing by framing it as a per-prompt calculation rather than a pricing catalog lookup.
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 cost estimation but provides no explicit guidance on when to prefer this over get_pricing or other siblings. No exclusions or alternative tool mentions, so it falls to implied usage.
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?
The description aligns with the readOnlyHint annotation, adding that the output is MCP configuration JSON for specific clients. However, it does not go beyond that to clarify side effects or detailed behavior; the readOnlyHint already covers the safety profile, so the description adds moderate value.
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 a single, clear, front-loaded sentence that states exactly what the tool does with no unnecessary words or repetition.
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 one-parameter tool with an enum and readOnlyHint annotation, the description adequately conveys the tool's purpose and output type. It does not explain the exact structure of the generated JSON, but the output schema absence is compensated by the clear mention of 'MCP configuration JSON' and the target list.
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 provides 100% coverage of the single 'target' parameter, including an enum and description. The tool description adds no additional parameter-level meaning beyond what the schema states, so the baseline of 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 uses a specific verb ('Generate') and resource ('MCP configuration JSON'), and explicitly lists three target clients. This clearly distinguishes the tool from siblings like chat, model listing, or connection testing.
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 clearly implies when to use the tool (when generating config for Claude Desktop, Claude Code, or Codex CLI), but does not provide explicit exclusions or alternative tool references. Given the sibling list, the use case is unambiguous, but a bit more direct guidance would make it a 5.
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 declare readOnlyHint=true, and the description adds that it shows a triple preset configuration. This adds content context but does not disclose additional behavioral traits like output format, latency, or any side effects. The description is consistent with the annotation, so 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short, direct sentence that front-loads the verb and resource. Every word contributes to meaning with no redundancy or filler.
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 zero-parameter read-only tool with no output schema, the description is sufficient to understand purpose and content. It could add a hint about the exact structure of the returned presets, but the current text makes it clear enough. The low complexity and existing annotation support completeness.
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?
There are zero parameters, so the schema is complete and the description naturally adds no parameter-specific info. However, the description enriches understanding by specifying that the output contains planner/coder/reviewer roles, which is valuable beyond the empty schema. Baseline 4 for zero-parameter tools.
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 has a specific verb ('Show') and a specific resource ('triple preset configuration') with clarifying context ('for CODE-MAS orchestrator'). It clearly distinguishes from sibling tools like get_usage, get_pricing, and get_model_info.
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?
Usage is implied from the name and description: if you want to see the configured presets, use this. However, there is no explicit 'when to use' or 'when not to use' guidance, nor any mention of alternatives such as export_config or compare_models.
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?
With readOnlyHint:true, the annotation already signals a safe read operation. The description adds useful context beyond that: the pricing is 'public' (implying no special auth required) and denominated in USD per 1M tokens. This helps the agent understand the nature of the data returned, going beyond a simple read-only hint.
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 one concise sentence that front-loads the core purpose (getting pricing) and includes key qualifiers (public, USD per 1M tokens). Every word adds value, with no redundant or filler content.
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 simplicity (one optional parameter, no output schema, read-only annotation), the description is largely complete. It clearly states what the tool returns (pricing in a specific unit) but does not specify whether omitting 'model' returns all models or exactly how the result is structured. This minor gap prevents a perfect score.
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 already provides a complete description for the only parameter ('model' as an optional string). Since schema coverage is 100%, the baseline is 3. The tool description does not add any additional meaning about the parameter, so it neither enhances nor detracts from 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's function: to get public retail pricing for AIOrouter models, with specific unit (USD per 1M tokens). This distinguishes it from sibling tools like aiorouter_estimate_cost or aiorouter_list_models, making the purpose 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 when to use this tool (when needing list prices) but does not explicitly mention alternatives or situations where another tool would be more appropriate, such as aiorouter_estimate_cost for custom cost calculations. There are no exclusions or prerequisites, so it relies on the agent inferring the usage context.
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 declare readOnlyHint=true, so the safety is covered. The description adds context about comparison dimensions (provider, pricing, etc.) but does not disclose other behavioral traits like error handling, rate limits, or reliance on external data.
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 a single, well-structured sentence that front-loads the action and scope. Every word adds value, and it is easy to parse.
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 simplicity (one parameter, no output schema), the description sufficiently covers the purpose, constraints, and expected comparison fields. No critical information is missing.
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 100% as the 'models' parameter has a clear description with min/max. The tool description adds no extra parameter meaning beyond what the schema already provides, so baseline 3 is appropriate.
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 specific action ('Compare'), the resource ('AIOrouter models'), and the exact scope (2-5 models side-by-side) along with the attributes compared. This distinguishes it from siblings like list_models or get_model_info.
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 explicitly defines when to use this tool: when comparing 2-5 models. It provides clear context without mentioning alternatives, but the context is unambiguous for a comparison task.
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 declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds that it checks API key validity but does not describe what the result looks like (e.g., success/error format) or any potential network side effects. Given the annotations cover safety, 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?
A single, focused sentence with no waste. It front-loads the core action and is appropriately sized for a trivial connection test.
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 zero-parameter health check tool with a readOnly annotation, the description is sufficient to convey the core action. However, since there is no output schema, it would benefit from stating what the response indicates (e.g., valid/invalid). Hence a 4.
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 tool has zero parameters, so the description does not need to explain any parameter semantics. Baseline for 0 params is 4, and the empty schema leaves nothing to clarify.
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 uses a specific verb ('test') and resource ('AIOrouter API key') and clearly distinguishes this from sibling tools like aiorouter_chat or aiorouter_list_models. It unambiguously states the purpose of validating the API key.
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 clearly implies the tool is for verifying API key validity before using other tools, but it does not explicitly state when to use it vs alternatives or provide exclusion criteria. The context is clear enough for a simple health check, but lacks explicit when-not-to-use 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?
Annotations declare readOnlyHint=true, and the description describes a read-only operation with no contradiction. The description adds value beyond the annotation by specifying the exact fields returned (provider, context window, etc.), giving the agent expected output shape even without an output schema.
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 a single, front-loaded sentence: 'Get detailed info for a single AIOrouter model' immediately states the action, followed by a compact list of data fields. No wasted words; every element contributes to understanding the tool.
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?
For a simple single-parameter read-only tool, the description is complete. It covers the purpose, the scope (single model), and the return content (list of fields), compensating for the lack of an output schema. There are no hidden prerequisites or side effects to disclose.
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 fully describes the only parameter 'model' with an example ('deepseek-v4-pro'). The description doesn't add extra syntax or format details, so the schema carries the semantic load. Baseline 3 applies for high schema coverage.
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 a specific action ('Get detailed info') on a specific resource ('a single AIOrouter model') and enumerates the content (provider, context window, max output, capabilities, pricing, supported parameters). This distinguishes it from sibling tools like aiorouter_list_models (bulk list) and aiorouter_compare_models (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?
The description implies the right use case: when you need detailed info about one specific model. However, it does not explicitly mention alternatives or when not to use it (e.g., for a bulk list use aiorouter_list_models, for pricing-only use aiorouter_get_pricing). Clear context but no 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 already declare readOnlyHint=true, so the safety profile is covered. The description adds the scope 'for your API key', which is useful context, but it does not disclose return format, pagination, or rate limits. This matches the baseline for an annotated read-only tool with some added 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?
The description is a single concise sentence that front-loads the action and resource. Every word earns its place, with no redundancy or filler.
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 simplicity (no parameters, no output schema), the description is complete. It clearly states what the tool returns (usage, billing summary, subscription status), which is sufficient for the agent to understand the tool's purpose and output.
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 tool has zero parameters, so the baseline is 4. The description adds no parameter-specific details because none are needed; the schema is empty and coverage is 100% (trivially).
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 uses a specific verb 'Get' and clearly identifies the resource: usage, billing summary, and subscription status. It distinguishes itself from siblings like get_pricing and estimate_cost by focusing on current usage and billing rather than pricing or cost estimation.
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 clearly implies when to use this tool (to check current usage, billing, and subscription status) and provides enough context to differentiate from related tools like get_pricing or estimate_cost. However, it does not explicitly state when not to use it or name alternatives.
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?
Despite having only destructiveHint=false as an annotation, the description discloses the non-streaming limitation (MCP constraint) and the behavior of returning the model's full response. It also highlights the max_tokens cap, which is a meaningful constraint. This goes beyond the sparse annotations without contradicting them.
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 sentences with no filler. It front-loads the primary purpose and adds useful caveats (non-streaming, token limit) in a compact way. Every clause 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?
For a tool with no output schema and sparse annotations, the description covers the core action, response behavior, and a key limitation. It does not explain the exact response format or error cases, but the chat completion context makes the return type reasonably predictable. It is sufficient for an agent to use the tool correctly.
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 parameters are already well-documented. The description adds extra value by advising to increase max_tokens for long responses, which is a usage hint beyond the schema's plain range. It also reinforces the model parameter's source via aiorouter_list_models, though this lives in the schema as well.
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 uses a specific verb ("Send") and resource ("chat completion to any AIOrouter model"), clearly distinguishing this as the core execution tool among siblings like aiorouter_list_models and aiorouter_get_usage. It lists example models to add further specificity.
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 use (sending chat completions) and even includes a cross-reference to aiorouter_list_models via the model parameter description. It states the non-streaming limitation and advises increasing max_tokens for long responses, offering practical usage guidance. It does not explicitly name alternatives, but sibling tools are clearly informational or comparative.
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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