OpenRouter MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a distinct purpose: get single model details, list with basic filters, search with advanced filters, compare multiple, and cache refresh. No overlapping functionality; descriptions clearly differentiate them.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern using lowercase with underscores: list_models, search_models, get_model, compare_models, refresh_cache. No mixing of conventions.
Tool Count5/5With 5 tools, the server provides a focused set for model discovery and management. This is neither too few nor too many for the domain of querying model information from OpenRouter.
Completeness5/5The tool set covers all essential operations for interacting with OpenRouter models: listing, searching, getting details, comparing, and cache management. No obvious gaps like missing model capability queries, as search covers those.
Average 3.6/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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.jsonto 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as whether the operation is read-only, potential side effects, or output format. The minimal description lacks sufficient transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short with no wasted words. It lacks structure (e.g., sections) but remains efficient. A bit more detail could be added without compromising conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param) and presence of output schema, the description is incomplete. It fails to describe what 'side by side' means in the output, whether it shows differences or full models, or how results are presented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains that model_ids is 'comma-separated model IDs,' adding format context beyond the schema (which only specifies type string). However, with 0% schema description coverage, more detail on parameter constraints would improve clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Compare multiple models side by side,' specifying the verb 'compare' and the resource 'multiple models.' It distinguishes itself from sibling tools like get_model (single) and list_models (list) by implying a comparative operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does 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. While the purpose implies it's for comparing multiple models, there is no guidance on exclusions or when-not-to-use.
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?
No annotations are provided, so the description carries the full burden. It discloses the filtering and sorting parameters, implying a read-only listing operation. However, it does not mention rate limits, pagination, result limits, or any side effects. For a simple list tool, basic behavioral traits are partially covered but not comprehensively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a clear one-liner purpose followed by parameter details in a readable arg list. No unnecessary words. However, the parameter list could be formatted more clearly (e.g., bullet points) for machine parsing, though it remains human-readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 optional params, output schema exists), the description is functionally sufficient but lacks context about when to invoke it relative to siblings. It does not mention that it returns a full list or the default behavior (e.g., all modalities). The output schema likely covers return format, but usage context is minimal.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description adds meaning by listing possible values for 'modality' (text, image, audio, embeddings, all) and 'sort_by' (name, created, price, context_length). However, it does not explain what each sort option means (e.g., alphabetical, date, cost, token limit), leaving some ambiguity. The added value compensates for schema gaps but is still minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List models available on OpenRouter.' This directly distinguishes it from siblings like 'compare_models' (comparison), 'get_model' (specific model), and 'search_models' (search). The verb 'List' plus resource 'models' is 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 Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives (e.g., search_models, compare_models). It only describes the parameters, leaving the agent to infer the use case. Without explicit context, the agent may struggle to choose the appropriate tool.
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?
No annotations are provided, so the description must disclose behavior. It states a simple read operation with no side effects, which is accurate but lacks details on potential errors (e.g., if model_id doesn't exist) or caching behavior. The safety profile is implied but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise – one line plus a parameter note – without wasted words. While it lacks formal structure, it efficiently conveys the essential information for a simple getter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and an output schema, the description covers the core purpose and parameter explanation. Minor omissions like error handling or existence checks could be included, but overall it is sufficient for a straightforward operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds an example value for model_id ('anthropic/claude-sonnet-4.6') and clarifies it must be a model slug. This significantly improves understanding beyond the schema's bare 'Model Id' title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves detailed info for one model, using a specific verb ('Get') and resource ('detailed info for one model'). It distinguishes itself from siblings like list_models (multiple models) and 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 Guidelines2/5Does 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 like list_models or search_models. The description only states what it does, leaving the agent to infer context without any directional cues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden, but it only explains parameter semantics. It does not disclose side effects, authentication requirements, rate limits, or how filters combine. The tool's effect on the system is opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and then lists parameters in a clear, compact format. Every line provides essential information without redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 7 parameters and the existence of an output schema, the description covers parameter purposes but omits context on output format, default behavior, and limitations. It is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description adds crucial meaning for each parameter, e.g., 'Free-text search in model name/id/description'. It compensates well for the schema's lack of descriptions, though individual parameter explanations are brief.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search and filter OpenRouter models,' identifying both the action (search and filter) and the resource. It is distinct from sibling tools like list_models and get_model, which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists filters but does not provide explicit guidance on when to use this tool versus siblings. No alternatives or exclusion criteria are mentioned, only implied through the list of parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The description mentions 'force refresh' but does not disclose potential side effects (e.g., impact on ongoing requests, rate limits, or whether it is idempotent), leaving significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the key action. Every word contributes to the meaning, with no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema exists, the description is largely complete for its purpose. It explains the action, and the output schema can document return values. However, it lacks any usage context or behavioral notes that could be useful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the input schema provides complete coverage. Per guidelines, baseline is 4. The description adds no additional parameter meaning, but that is acceptable given no parameters exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool refreshes the model cache from OpenRouter. The verb 'refresh' and resource 'model cache' are specific, and it distinguishes from sibling tools that compare, get, list, or search models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when cache is stale but does not provide explicit guidance on when to use or when not to use, nor does it mention alternatives among siblings. The context is clear but lacks exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/lumishoang/openrouter-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server