fugu-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation2/5
Both tools serve essentially the same purpose—getting a second opinion from Sakana Fugu. The descriptions differ slightly in phrasing but offer the same core functionality, making it unclear when to use one versus the other.
Naming Consistency2/5Tool names are inconsistent: 'ask_fugu' follows a verb_noun pattern, while 'fugu_second_opinion' uses a noun_adjective pattern. Mixed naming conventions reduce predictability.
Tool Count2/5With only two tools covering a single niche (second opinions), the set feels unnecessarily thin. The functionality could easily be served by one well-designed tool.
Completeness1/5The tool surface is severely incomplete for any meaningful server scope. Not only is there no variety of operations, but the two tools overlap almost entirely, leaving no discernible domain coverage beyond a single redundant capability.
Average 4.5/5 across 2 of 2 tools scored.
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
- 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?
No annotations are provided, so the description carries the full burden. It discloses that Fugu is a separate LLM, acts as a skeptical senior reviewer, and returns 'concrete issues, risks, and improvements.' It also notes that Fugu cannot see the repo or conversation, and that all context must be in 'content.' While it could mention potential timeouts or output format, the description provides sufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (3-4 sentences) and front-loaded with the core purpose. Every sentence adds value: purpose, best use case, constraint, warning, and performance note. No unnecessary words.
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 absence of an output schema, the description explains return values as 'concrete issues, risks, and improvements.' This is sufficient but could be more specific (e.g., format). The tool has three well-documented parameters and clear usage guidance, leaving minimal gaps. A minor omission is lack of mention of rate limits or output length.
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%, with each parameter having a detailed description in the schema (e.g., 'content' includes examples of what to review). The tool description does not add additional parameter-level semantics beyond the schema, 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.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a rigorous SECOND OPINION from Sakana Fugu on a discrete piece of work.' It specifies the verb (get), resource (second opinion), and scope (discrete piece), and differentiates from the sibling 'ask_fugu' by emphasizing cross-checking and skepticism.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance: 'Best for cross-checking your own output with a different model on a self-contained artifact.' It also states when not to use it: 'Do NOT use it for interactive/iterative repo work or anything needing local file access.' Additionally, it mentions performance ('Calls can be slow'), giving clear context for use.
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 given, so description handles all burden. Discloses that Fugu is a separate LLM, cannot see repo/files/conversation, and calls can be slow (tens of seconds). Does not mention read-only/destructive but it's implied as a LLM call. Adequate for safe use.
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 well-structured paragraphs, front-loaded with purpose and key constraints. Every sentence adds value; no fluff. Efficiently conveys necessary information.
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 and no annotations, the description covers purpose, usage guidelines, parameter requirements, behavioral traits (slowness, self-contained), and limitations. Complete for an agent to select and invoke 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 coverage is 100% with descriptive parameter definitions. Description adds minor context (e.g., 'Include all context Fugu needs') but largely overlaps with schema. At baseline of 3 for high coverage; no significant value beyond schema.
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?
Title and description clearly state it's for a second opinion from a separate LLM. Specifies exact use cases (hard self-contained problems, tricky algorithms, design trade-offs) and distinguishes from sibling by explicitly stating what it is not for (interactive repo work, commands, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists when to use (hard problems, gut-check) and when not (iterative repo work, multi-step edits). Provides context that prompt must be self-contained, and advises doing other tasks yourself. No ambiguity.
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: