wigolo

by KnockOutEZ

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Server Quality Checklist

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Latest release: v0.2.1
Disambiguation5/5
Each tool targets a distinct operation: single-URL fetch, web search, cache search, similarity search, multi-step research, flexible agent, and diff. There is no overlap or ambiguity in their purposes.
Naming Consistency4/5
Most tool names are single lowercase words (fetch, search, cache, research, agent, diff), but find_similar breaks the pattern with an underscore. The naming is generally clear and predictable.
Tool Count5/5
With 7 tools, the server is well-scoped for its purpose of web content retrieval and analysis. Each tool provides distinct value, and the count is neither too few nor too many.
Completeness5/5
The tool set covers the full lifecycle of web information gathering: fetching, searching, caching, similarity finding, deep research, flexible agent-based gathering, and diff comparison. There are no obvious gaps for the intended domain.
Average 4.7/5 across 6 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 20 of 41 community issues answered or closed in the last 6 months
- 1012 commits in the last 12 weeks
- Last stable release on July 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under AGPL 3.0.
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.
This repository includes a glama.json configuration file.
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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

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It thoroughly explains caching behavior, section extraction smart truncation, auth reuse, JS rendering, mode differences, return fields (including fetch_method, http_status, content_completeness), site-specific extractors, and edge cases like empty markdown when section doesn't match. All relevant behavioral traits are disclosed.
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 longer than average but well organized: a one-sentence summary, a paragraph on key parameters, then return fields, site-specific behaviors, and edge cases. Every sentence adds value, though minor trimming could improve succinctness.
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 15 parameters, 100% schema coverage, and no output schema, the description is remarkably complete. It covers all key behaviors, parameter interactions, return fields, sibling differentiation, and edge cases. No gaps are apparent for an AI 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 coverage is 100%, so baseline is 3. The description adds significant meaning: explains the interaction between max_tokens_out and max_chars, the effect of include_full_markdown default, the meanings of mode values, and how actions bypass HTTP-first routing. This goes well beyond the schema descriptions.
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 'Fetch a single URL and return clean markdown,' specifying verb and resource. It distinguishes from sister tool 'WebFetch' by listing advantages like local-cache reuse, authenticated pages, JS-rendered SPAs, and structured metadata. This makes the tool's unique purpose immediately clear.
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 says 'Use when you already have a URL' and advises 'Prefer over built-in WebFetch...' providing clear context for when to use this tool over alternatives. It does not explicitly list exclusions, but the guidance is strong.
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?
No annotations provided, so description carries full burden. Discloses default behavior, brand collision warning, conditional freshness_signal, mode behavior, and response shape. Thorough coverage of edge cases.
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?
Well-structured: purpose sentence, usage recommendation, bullet-like parameter list, always-emitted fields. Front-loaded with key info. Slightly long but justified by complexity; every sentence adds value.
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 28-parameter tool with no output schema, description is remarkably complete: explains all output fields, parameter interactions, and edge cases (brand collision, conditional signals). Leaves no major gaps.
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 coverage is 100%, baseline 3. Description adds significant value: explains defaults (max_results=5), behavioral nuances (time_range is precision-boost not hard bound), and extra output context (image fields, query_understanding).
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?
States 'Search the web' and clearly describes default output (scored evidence excerpts + citations) and optional full markdown. Differentiates from built-in WebSearch via local cache, telemetry, and scoring.
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?
Explicitly recommends this tool over built-in WebSearch for caching/telemetry/scoring. Provides extensive parameter usage guidance (e.g., scope domains for library queries, time_range precision vs. from_date/to_date). Missing explicit when-not-to-use scenarios but strong overall.
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?
With no annotations provided, the description carries full burden. It details the pipeline (plan, search+fetch in parallel, optional extraction, synthesis), mentions full step transparency via steps[], explains LLM-optional behavior, and discloses fallback to keyword extraction. It also covers budget and output controls.
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 well-structured with a high-level summary, LLM-optional note, parameter details, pipeline, and return value list. It is front-loaded with purpose. However, it is somewhat lengthy and includes minor details (e.g., tokenizer drift) that could be omitted for conciseness.
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 complexity (9 parameters, nested objects, no output schema), the description is thorough. It explains the pipeline, behavior without synthesis LLM, return fields (result, sources[], steps[], etc.), and parameter effects. It covers edge cases like max_tokens_out vs max_chars and citation format options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds significant value beyond the schema. It explains the purpose of each key parameter in context (e.g., 'prompt: NL description of what to gather'), provides defaults (max_pages=10, max_time_ms=60000), and describes citation_format options with examples.
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 starts with 'Natural-language data gathering across sources', clearly stating the verb (gather) and resource (sources). It distinguishes the tool from siblings like fetch and search by describing a multi-step pipeline (plan, parallel fetch, extract, synthesize).
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 explains when to use this tool (for NL data gathering with optional schema) and provides context for LLM-optional behavior. It tells the agent to configure an LLM key for best results and warns against presenting raw step logs. However, it does not explicitly list when not to use it or compare directly to siblings.
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?
With no annotations, the description fully discloses behavior: decomposes into sub-queries, parallel search, fetching, synthesis, and returns detailed structures (report, brief with key_findings/highlights/sections, gaps). It also explains optional LLM dependency and parameter effects on timing and output.
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 long but well-structured, with a clear summary first, then LLM-optional nuance, then parameter details, then return shape. Every sentence adds value, though it could be slightly more terse without losing clarity.
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 complex tool with 10 parameters, no output schema, and nested objects, the description is remarkably complete. It covers return fields (citations, sources, brief with topics/highlights/gaps), phases, and optional LLM behavior. Missing only error/exception details and explicit prerequisites (API key mentioned as optional).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant meaning: explains depth time estimates, max_sources usage, citation formats, include_full_markdown effects, and schema parameter for structuring output. This goes beyond the schema's basic descriptions.
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 is for multi-step research on complex questions, decomposing into sub-queries, searching, and synthesizing a cited report. It distinguishes itself from manual chaining of 'search' and 'fetch' by offering multi-source synthesis.
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 explains when to use this tool (complex questions requiring multi-source synthesis) and how it compares to alternatives (beats chaining search+fetch manually). It also provides guidance on handling outputs when no synthesis LLM is configured. However, it does not explicitly state when not to use it.
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?
With no annotations, the description fully discloses behavior: no network round-trip, persistence across sessions, FTS5 search capabilities, support for globs, date filtering, stats, and clear. It also explains the mode parameter's fallback strategy and the check_changes feature. No gaps or contradictions.
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 front-loaded with purpose and guidance, then uses a bullet list for key parameters, and ends with a summary line. Every sentence provides essential information without redundancy. Efficient and well-organized.
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 9 parameters with no output schema, the description covers all behaviors including complex ones like hybrid search and change detection. It sufficiently equips an agent to use the tool correctly without ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, yet the description adds significant value: it details FTS5 operators for query, explains hybrid vs fts mode, requires filters for clear, describes stats output, and elaborates on check_changes. This greatly aids correct usage.
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 purpose: 'Search previously fetched content without hitting the network.' It specifies the verb (search) and resource (cached content), and distinguishes itself from sibling tools like fetch and search by directing the user to run this before them.
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 instructs to 'Run this BEFORE any search/fetch' and implies it's for instant cache hits. While it doesn't explicitly list alternatives for cache misses, the context is clear enough. Lacks explicit 'when not to use' but still provides strong guidance.
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?
Without annotations, the description fully discloses important behaviors: cache miss returns structured error, truncation at 5000 lines with explicit flag, and never silent degradation. Also explains return value structure and default behaviors.
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?
Well-structured with bullet points for key parameters and a clear explanation of return value. Every sentence provides necessary information without redundancy. Efficient yet comprehensive.
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 complexity (4 params with nested objects, enums, and multiple edge cases like caching and truncation) and no output schema, the description fully covers behavior, input options, and output structure. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% but the description significantly adds value: explains which fields are required, provides examples/hints for enums (e.g., 'unified (default, git-style patch)'), describes nested object constraints, and clarifies cache semantics 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?
Clearly states 'Compute a diff between two markdown bodies or two URL fetches', specifying the action, resource, and domain. Distinguishes effectively from sibling tools (fetch, search, etc.) which do not perform diffing.
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?
Provides detailed parameter explanations including cache behavior, output options, and granularity. While there is no explicit 'when to use' vs 'when not to', the context makes it clear for diffing tasks. Lacks direct comparison to alternatives.
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?
With no annotations, the description fully discloses behavioral traits: three signals fused via RRF, cold_start signal for weak cache, opt-in ranking_debug, parameter budget controls, and the shape of the response (results, method, cache_hits, etc.). 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 well-structured, starting with a one-sentence summary, followed by usage guidelines, then parameter details. It is front-loaded with the key purpose. However, it is somewhat dense and verbose, especially in the parameter section, which could be more concise.
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 complexity (13 parameters, no output schema, three signals), the description is thorough and complete. It covers what the tool returns, how parameters affect behavior, and caveats like cold cache. It provides enough information for an AI agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant value beyond parameter names and types. It explains the interplay between parameters (e.g., include_ranking_debug, cold_start, threshold behavior), provides context for url vs concept, and describes how parameters affect the fusion logic.
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 'Find content related to a URL or concept', specifying the resource and scope. It distinguishes from siblings by emphasizing 'Best after a successful crawl/fetch' and referencing alternative tools like crawl/fetch for warming the cache.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance on when to use the tool ('Best after a successful crawl/fetch') and when not to use it ('concept-only queries on a cold cache often return 0-2 weak matches'). It advises warming the cache first via crawl/fetch and specifies that either url or concept should be passed.
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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