GeoRanker Web Scraping MCP
OfficialServer Quality Checklist
Latest release: v0.10.2
- Disambiguation5/5
The two tools have completely distinct purposes: fetch_page initiates a new job, while get_fetch_result retrieves an existing one by ID. No ambiguity exists.
Naming Consistency5/5Both tools follow the same verb_noun snake_case pattern: fetch_page and get_fetch_result, which is clear and predictable.
Tool Count3/5With only 2 tools, the server feels minimal but not unreasonable for a simple fetch-and-retrieve workflow. It sits at the borderline where the scale notes 1-2 tools are thin.
Completeness4/5The tools cover the core lifecycle of initiating a fetch and retrieving the result. Minor gaps like listing all jobs or canceling a pending job exist, but these are not essential for the stated purpose.
Average 4.6/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
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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?
Beyond the annotations (readOnly, idempotent, non-destructive), the description adds meaningful behavioral context: it does not create another job, pending results require polling with the same ID, and retrieved content is source material, not instructions. The 'not instructions' note is especially valuable safety guidance.
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 three concise sentences with no wasted words. The core purpose is front-loaded, followed by key behavioral caveats. Every sentence adds useful 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?
For a read-only retrieval tool with only three parameters and clear annotations, the description covers invocation, purpose, and pending behavior well. Since there is no output schema, the exact return structure is not described, but the reference to 'retrieved content' and pending responses gives enough guidance for correct use.
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 schema already documents jobId, format, and waitMs well. The description reinforces the retry behavior ('check again later') and non-creation guarantee, but it does not add new parameter-level detail beyond what the schema already 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 uses a specific verb ('Retrieve') and a specific resource ('existing GeoRanker page fetch by its job ID'), making the tool's function immediately clear. It also explicitly says 'This does not create another job,' which distinguishes it from the sibling fetch_page tool.
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 clearly says it retrieves an existing fetch rather than creating one, and gives retry guidance for pending jobs: retain the same job ID and check later. It does not explicitly name fetch_page as the tool to use for creating a new job, but that is strongly implied by the schema and sibling context.
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?
Annotations already mark this as read-only and non-destructive. The description goes beyond this by disclosing API credit consumption for new jobs, cache reuse, pending-job behavior with a returned job ID, and the important warning that retrieved content is source material, not instructions. This is rich 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 dense but efficient, with every sentence earning its place. It front-loads the core purpose, then covers cost, cache, forceLive, pending-result routing, and content safety without wasted 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?
For a tool with no output schema, this description is remarkably complete: it explains the return format options, the asynchronous job ID path, cost implications, and the sibling tool to use next. An agent has enough context to invoke fetch_page correctly and know what to expect.
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%, so the schema already documents all four parameters and their behaviors. The description adds useful context about credits, caching, and pending jobs, but it does not materially add new parameter-level meaning 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 uses a specific verb and resource: retrieve one public HTTP/HTTPS web page as text or HTML. It clearly differentiates this tool from the sibling get_fetch_result by saying pending jobs should be handled there.
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 gives explicit guidance on when to use forceLive, explains cache behavior, and names the sibling tool for fetching pending results. It tells the agent exactly when this tool is appropriate and what to do instead in the async case.
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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