gupy-candidate-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct stage of the candidate workflow: searching jobs, viewing details, loading a profile, analyzing fit, preparing application materials, and monitoring deadlines. There is no overlapping or ambiguous functionality.
Naming Consistency4/5
Most tools follow a verb_noun pattern (buscar_vagas, carregar_perfil, analisar_aderencia, preparar, monitorar), but 'detalhe_vaga' uses a noun phrase instead of a verb, creating a minor inconsistency.
Tool Count5/5
Six tools cover the core application lifecycle without being excessive or too sparse. Each tool serves a clear purpose within the candidate-oriented workflow.
Completeness4/5
The toolset covers searching, profiling, fit analysis, preparation, and monitoring, but lacks a tool for actually submitting the application, which is a notable gap. However, the existing tools still support a coherent end-to-end assistance flow.
Average 3.3/5 across 6 of 6 tools scored. Lowest: 2.7/5.
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 is passing
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.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it only mentions 'filtros opcionais'. It does not state whether the operation is read-only, how results are paginated or returned, or any external service implications. This leaves significant behavioral ambiguity.
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 one-sentence description is efficient and front-loaded, immediately conveying the core purpose. However, it is extremely terse and omits details that could be added without bloating the text, making it appropriately concise for its limited scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is incomplete for a tool with 7 optional parameters, no annotations, and no output schema. It fails to mention return values, pagination behavior, or defaults beyond a few schema descriptions, leaving critical gaps for an agent to invoke and interpret results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific meaning beyond the schema, merely noting 'filtros opcionais'. With 3 of 7 parameters lacking schema descriptions (jobTypes, companyId, and the workplaceTypes array), the description fails to compensate for these gaps, leaving the agent without clarity on those parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: searching public job openings on Gupy with optional filters. The verb 'Busca' and resource 'vagas públicas' make it distinct from sibling tools like 'detalhe_vaga' or 'monitorar', though it does not explicitly compare with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states what it does, leaving the agent to infer usage context. There is no specification of when to prefer this over 'detalhe_vaga' or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral disclosure. It only mentions 'analisa' (analyzes), omitting input requirements, output format, side effects, or permissions. This is insufficient for a tool with no other 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler or redundancy. It earns its place by stating the core purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and minimal description, yet it does not explain what the analysis returns, how it works, or what inputs are needed. For a tool with two parameters and no structured output, this is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 50% (caminhoPerfil is described), and the tool description adds no parameter semantics. The required vagaId parameter is left undocumented, and the optional nature of caminhoPerfil is not explained.
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 function: analyzing profile-to-vacancy adherence. This is distinct from siblings like carregar_perfil (load profile) and detalhe_vaga (view vacancy), using a specific verb and resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor any exclusions or prerequisites. The intended usage is only implied by the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states that the tool monitors deadlines but does not clarify what the tool returns, whether it is a read-only operation, whether it triggers notifications, or any side effects. The term 'monitor' is ambiguous without additional context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no unnecessary words. It is concise and front-loaded with the core action and object.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations and output schema, the description is incomplete. It does not explain the output/return behavior, potential errors, or when to prefer this tool over similar ones. For a simple boolean or monitoring tool, this might be tolerable, but the lack of any usage context prevents a higher score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 100% ('Lista de IDs de vagas'), and the description refers to 'uma lista de vagas', directly mapping to the vagas parameter. However, the description does not add meaningful detail beyond the schema, such as format requirements or edge cases, so it earns the baseline score for high schema coverage.
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 function: 'Monitora os prazos de inscrição de uma lista de vagas' (monitors application deadlines for a list of vacancies). It uses a specific verb ('monitora') and a specific resource ('prazos de inscrição de uma lista de vagas'), making it distinct from sibling tools like buscar_vagas (search) or detalhe_vaga (detail).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or comparison with sibling tools such as detalhe_vaga or analisar_aderencia, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations to provide safety or side-effect hints, so the description carries the full burden. It only mentions loading and validating without disclosing whether it reads remote data, modifies anything, or what happens on validation failure. The word 'valida' is ambiguous about behavior (e.g., does it throw, return a result, or log outputs?).
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 a single sentence that is front-loaded with the main verb and object. It avoids unnecessary words or repetition, making it highly concise for a simple one-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter, no annotations, and no output schema, the description is minimally adequate but leaves a key gap: it does not state what the tool returns (e.g., the loaded profile, a validation result, success/failure). An agent would not know what to expect after invoking it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the single parameter 'caminho' as an absolute path to the JSON profile, and schema description coverage is 100%. The description adds no new meaning beyond repeating that the input is a JSON file, so the baseline of 3 is appropriate.
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 'Carrega e valida' (loads and validates) and the resource 'perfil do candidato' from a JSON file. It distinguishes itself from sibling tools like 'buscar_vagas' (search jobs) and 'analisar_aderencia' (analyze adherence), which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or relationships to sibling tools. The agent is left to infer usage from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states the action ('Gera a entrega') without noting side effects, prerequisites (e.g., whether a profile must be loaded), or what happens to the generated output. This is a significant gap for a generation tool.
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 a single, clear sentence with no redundant information. It is concise and front-loaded, leaving no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description is incomplete. It does not describe the generated delivery's format, return value, or any prerequisites for use, which are essential for an AI agent to confidently invoke the tool. The low complexity does not excuse the missing details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33%, so the description must compensate. It implicitly explains vagaId as the vacancy and enumerates entrega options, but it does not address the optional caminhoPerfil parameter or explain how each parameter affects the output. The description adds general context but not detailed parameter semantics.
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 function with a specific verb ('Gera'), the resource ('a entrega'), and the deliverable types (currículo, carta, formulário). It also specifies the scope ('para uma vaga com base no perfil'), which distinguishes it from sibling tools like buscar_vagas or carregar_perfil.
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 provides clear context for when to use the tool (generating a delivery for a vacancy based on a profile) without explicitly stating alternatives or exclusions. It implicitly differentiates from siblings but lacks direct 'use this instead of X' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. 'Detalha' suggests a read-only operation, but it does not mention error behavior, required permissions, or the structure of the returned details. For a simple read, it provides basic clarity but lacks richer 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the action, object, and key qualifier without any wasted words. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple one-parameter schema and lack of output schema/annotations, the description is minimal but adequate. However, it does not clarify what 'detalha' returns (e.g., full details vs. summary) or how it differs from 'buscar_vagas' in usage, leaving some gaps for a complete understanding.
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?
The schema has one parameter 'id' with no description, but the description states 'pelo ID', clarifying that the integer parameter is the vacancy's identifier. This adds essential meaning beyond the bare schema, though it does not elaborate on format constraints.
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 uses a specific verb ('Detalha') and resource ('vaga') and clearly indicates the operation is by ID. This distinguishes it from sibling tools like 'buscar_vagas', which implies searching rather than fetching a single record.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a vacancy ID and need detailed information, but it does not explicitly state when to use this tool versus alternatives like 'buscar_vagas'. There are no exclusions or alternative references.
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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