genderize
Server Details
Genderize MCP — gender prediction from first name (genderize.io, free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-genderize
- GitHub Stars
- 0
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Tool Definition Quality
Average 3.9/5 across 2 of 2 tools scored.
The two tools are clearly distinct in purpose: one predicts gender globally, while the other does so for a specific country. The descriptions make this distinction explicit, though both share the same core functionality which could cause minor confusion if an agent needs to choose between them without clear country context.
Both tools follow a consistent verb_noun pattern with 'predict_gender' as the base, and the second tool adds a modifier ('_country') to indicate specialization. This naming scheme is clear, predictable, and adheres to a single convention throughout.
With only two tools, the server is minimal but appropriate for its narrow domain of gender prediction from names. It covers the essential global and country-specific cases, though it might feel slightly thin if more granular options (e.g., by region or language) were expected.
For a gender prediction service, the toolset covers the primary use cases: global predictions and country-specific calibrations. However, there are minor gaps, such as no tool for batch processing multiple names or handling ambiguous cases, which agents might need to work around.
Available Tools
2 toolspredict_genderAInspect
Predict the most likely gender of a person based on their first name, using global data from genderize.io. Returns gender ("male" or "female"), probability (0–1), and sample size.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | First name to predict gender for. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the data source ('genderize.io'), return values (gender, probability, sample size), and probability range (0–1). However, it lacks details on error handling, rate limits, or accuracy limitations, which are important for a prediction tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently covers purpose, data source, and return values without unnecessary words. It is front-loaded with the core function and provides essential details concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is reasonably complete. It explains what the tool does, the data source, and the return format. However, it could benefit from mentioning potential limitations or error cases to fully guide an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'name' clearly documented as 'First name to predict gender for.' The description adds no additional parameter semantics beyond what the schema provides, such as format constraints or examples. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('predict'), resource ('gender of a person'), and scope ('based on their first name, using global data from genderize.io'). It distinguishes from the sibling tool 'predict_gender_country' by specifying 'global data' rather than country-specific data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'first name' and 'global data', which suggests this tool is for general predictions without country filtering. However, it does not explicitly state when to use this vs. the sibling 'predict_gender_country' or provide exclusion criteria, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
predict_gender_countryAInspect
Predict the most likely gender of a person based on their first name, calibrated to a specific country.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | First name to predict gender for. | |
| country_code | Yes | ISO 3166-1 alpha-2 country code (e.g. "US", "GB", "DE") to localize the prediction. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it states the prediction is 'most likely' (implying probabilistic output) and 'calibrated to a specific country', it doesn't disclose important behavioral aspects like accuracy rates, confidence scores, data sources, limitations (e.g., handling of unisex names), or what happens with invalid inputs. For a prediction tool with zero annotation coverage, this represents significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-constructed sentence that efficiently communicates the core functionality. Every word earns its place - 'predict', 'most likely gender', 'person', 'first name', 'calibrated', 'specific country' - with no redundant information. It's front-loaded with the main purpose and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, prediction functionality) and the absence of both annotations and an output schema, the description is minimally adequate. It covers the basic purpose and differentiator but lacks important context about behavioral characteristics, output format, and limitations. The description should do more to compensate for the missing structured information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already fully documents both parameters. The description adds marginal value by mentioning 'first name' and 'specific country' which aligns with the schema, but doesn't provide additional semantic context beyond what's already in the parameter descriptions. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('predict the most likely gender'), target resource ('person based on their first name'), and key differentiator ('calibrated to a specific country') that distinguishes it from the sibling tool 'predict_gender'. It uses precise language that leaves no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly provides usage context by specifying 'calibrated to a specific country', suggesting this tool should be used when country-specific gender prediction is needed. However, it doesn't explicitly state when to use this tool versus the sibling 'predict_gender' tool, nor does it provide any exclusion criteria or alternative scenarios.
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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