opencritic-mcp
Server Quality Checklist
Latest release: v1.0.1
- Disambiguation5/5
Each tool has a distinct purpose: search_game finds games by name, get_game retrieves full details for a specific game, and get_game_reviews fetches individual critic reviews. There is no overlap in functionality, and the descriptions clearly indicate when to use each.
Naming Consistency5/5All tool names follow the same verb_noun pattern with lowercase snake_case: get_game, search_game, get_game_reviews. The pattern is consistent and predictable, making it easy to guess the function of each tool.
Tool Count5/5With only three tools, the server is well-scoped for its purpose of accessing OpenCritic data. Each tool covers a necessary step in the workflow (search, details, reviews), and no redundant or superfluous tools are present.
Completeness5/5The toolset provides a complete read-only workflow: search to find a game, then retrieve either its full details or its individual reviews. There are no obvious gaps, and the filter options add flexibility for platform-specific queries.
Average 4.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 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.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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It transparently lists the included data fields and the optional platform filter, making clear what the tool returns. It does not mention error handling or response structure, but the safety profile (read-only) is implicitly evident.
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?
Two concise sentences: first states the core function, second lists included fields and the optional filter. No filler, all information earns its place.
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?
Although there is no output schema, the description enumerates the return content (critic score, developer, etc.), making the tool's output comprehensible. The optional platform filter adds contextual detail, but ambiguity remains about how filtering affects the response (e.g., whether it affects the platforms array).
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?
Schema description coverage is 100%, so the baseline is 3. The description reinforces the optional platform filter with examples already present in the schema, but adds no new parameter semantics beyond what the schema provides.
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 full details and critic scores for a game by its OpenCritic ID, naming specific fields. This distinguishes it from siblings like search_game (searching) and get_game_reviews (reviews), which is evident from the context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after obtaining an OpenCritic ID from search_game (via the schema's parameter description and the text 'by its OpenCritic ID'). It also clarifies when the optional platform filter applies, but does not explicitly mention when to use alternatives like get_game_reviews.
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 no annotations, the description carries the full burden. It discloses what the tool returns (outlet, score, author, language, platforms, excerpt, link) and the optional platform filter. As a 'get' operation, it implies read-only behavior, but it does not explicitly state safety profile or error behavior. Still, for a simple retrieval, this is adequate.
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 two sentences, front-loaded with the primary action and clearly structured. Every sentence adds value: the first states the core purpose and return fields, the second explains the optional filter. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description lists the return fields. The schema covers parameters, defaults, and limits. The ID source is noted in the schema. For a simple read tool, the description together with schema provides sufficient context for an agent to use it correctly.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds the platform filter context and ID origin, but these are already present in the schema. No additional parameter semantics are provided beyond what the structured fields contain.
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 function with a specific verb and resource: 'Get individual critic reviews for a game by its OpenCritic ID.' It distinguishes itself from siblings (get_game, search_game) by focusing on reviews rather than general game info or search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the usage context (retrieve reviews for a known game ID) and the schema's id description explicitly says the ID comes from search_game. However, no explicit alternatives or exclusions are stated, though the purpose itself differentiates it from 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?
No annotations are provided, so the description must disclose behavior itself. It reveals that the search returns up to 10 matches with IDs, which is useful for understanding the output. It doesn't explain matching semantics, pagination, or error states, but the note about passing IDs to get_game/get_game_reviews offers practical behavioral context that goes beyond a bare search definition.
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 two sentences long, with the function stated immediately. The second sentence provides essential output information without unnecessary elaboration. It is concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and no output schema, but the description conveys the essential context: the result format (up to 10 matches with IDs) and how to use those results with sibling tools. This makes it complete for an agent to know when to use it and what to expect, despite the absence of an output schema.
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 already fully describes the only parameter 'query' with examples, so the schema coverage is 100%. The description adds no further parameter details beyond what's already in the schema. Therefore, no extra value is added from the description for parameter understanding.
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 function: 'Search for a game by name on OpenCritic.' The verb 'search' and the resource 'game by name' make it distinct from sibling tools like get_game and get_game_reviews, which are presumably for fetching specific details or reviews once an ID is known.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool: when you have a game title but need its OpenCritic ID. It explicitly states the IDs 'can be passed to get_game or get_game_reviews,' which guides the follow-up workflow. However, it doesn't explicitly say 'use this instead of X' 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.
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/brandikun/opencritic-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server