MCP DivulgaCandContas Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no overlap. The tools target different resources (candidates, accounts, years, municipalities, positions, elections) and actions (consult, list), making misselection unlikely. For example, consultar_candidato retrieves detailed candidate info while listar_candidatos_municipio lists candidates by municipality.
Naming Consistency5/5
Tool names follow a perfectly consistent verb_noun pattern in Portuguese (consultar_X, listar_X). All tools use snake_case uniformly, with clear verb choices (consultar for detailed queries, listar for enumerations). This predictability helps agents understand tool purposes at a glance.
Tool Count5/5
Seven tools is well-scoped for an electoral data server, covering core queries without bloat. Each tool earns its place by addressing distinct aspects like candidates, accounts, years, municipalities, positions, and election types. This count allows comprehensive coverage while remaining manageable.
Completeness4/5
The toolset provides strong coverage for querying electoral data, with tools for candidates, accounts, years, municipalities, positions, and elections. A minor gap exists in update/delete operations, but this is reasonable for a read-only public data server. Agents can retrieve comprehensive information without dead ends.
Average 3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
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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 provided, the description carries the full burden of behavioral disclosure. While 'consulta' implies a read-only operation, the description doesn't disclose important behavioral traits such as authentication requirements, rate limits, error conditions, or what format/scope the 'informações detalhadas' includes. For a tool with 4 required parameters and no annotation coverage, this represents a significant gap in behavioral transparency.
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, efficient sentence that gets straight to the point without any unnecessary words. It's appropriately sized for a tool with a clear purpose, and every word contributes to understanding what the tool does. The structure is front-loaded with the core functionality.
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 that this is a read operation with 4 required parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'informações detalhadas' includes, what format the response will be in, or any behavioral constraints. For a tool that requires multiple specific identifiers to function, the description should provide more context about the nature and scope of the returned information.
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 description doesn't add any parameter semantics beyond what's already in the input schema, which has 100% description coverage. The schema already documents all 4 required parameters with descriptions and constraints. Since schema_description_coverage is high (>80%), the baseline score of 3 is appropriate even though the description provides no additional parameter information.
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 purpose as 'Consulta informações detalhadas sobre um candidato específico' (Consult detailed information about a specific candidate), which provides a specific verb ('consulta') and resource ('candidato específico'). However, it doesn't explicitly differentiate from sibling tools like 'listar_candidatos_municipio' which lists candidates rather than retrieving detailed information about one specific candidate.
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 doesn't mention when to choose this tool over sibling tools like 'listar_candidatos_municipio' for listing candidates or other tools for different types of election data. No context about prerequisites or exclusions is provided.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'consulta' implies a read-only operation, the description doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what format the information takes. For a tool with 5 required parameters and no annotation coverage, this is a significant gap in 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, efficient sentence that states the tool's purpose without unnecessary words. It's appropriately sized for a lookup tool and front-loads the essential information. Every word earns its place in conveying the core functionality.
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 complexity (5 required parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what type of financial accountability information is returned, whether results are filtered/sorted, or what happens when parameters don't match existing records. For a tool that likely returns structured financial data, more context about the response format would be helpful.
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 100%, with all 5 parameters clearly documented in the schema (election code, year, municipality code, position code, candidate code). The description doesn't add any parameter semantics beyond what's already in the schema - it doesn't explain relationships between parameters or provide examples of valid values. The baseline score of 3 reflects adequate but minimal value addition.
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 purpose as 'Consulta informações sobre prestação de contas de um candidato' (Consult information about a candidate's financial accountability), which is a specific verb+resource combination. It distinguishes from siblings like 'consultar_candidato' (general candidate info) by focusing specifically on financial accountability information. However, it doesn't explicitly contrast with other siblings that might also provide financial data.
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 doesn't mention any prerequisites, context requirements, or comparison to sibling tools like 'consultar_candidato' (which might provide overlapping or complementary information). The agent must infer usage solely from the tool name and parameters.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Lista' implies a read-only operation, it doesn't specify whether this is a safe query, what permissions are needed, if there are rate limits, or the format/scope of the returned data. For a tool with zero annotation coverage, this is a significant gap in 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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized for a simple listing tool and front-loads the core functionality.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'cargos em disputa' entails (e.g., types of positions, data returned), behavioral traits like safety or limitations, or how results are structured, leaving the agent with insufficient context for reliable use.
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 description coverage is 100%, with both parameters ('eleicao' and 'municipio') documented as numeric codes. The description adds no additional semantic context beyond what the schema provides, such as example values or format details, so it meets the baseline for high schema coverage.
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 action ('Lista' - lists) and the resource ('cargos em disputa' - positions in dispute) with a specific scope ('em um município específico' - in a specific municipality). It distinguishes itself from siblings like 'listar_candidatos_municipio' by focusing on positions rather than candidates, though it doesn't explicitly mention this distinction.
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 like 'listar_candidatos_municipio' or 'consultar_candidato'. It mentions the municipality scope but doesn't specify prerequisites, exclusions, or complementary tools, 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?
No annotations are provided, so the description carries full burden. It describes a read operation ('Lista') but doesn't disclose behavioral traits like whether it returns all results or is paginated, what format the output takes, error conditions, or any rate limits. The description is minimal and lacks operational 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, efficient sentence that directly states the tool's purpose without any fluff. It is appropriately sized and front-loaded, with every word contributing to understanding the tool's function.
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 no annotations and no output schema, the description is incomplete for a tool with two required parameters. It lacks information on output format, error handling, or behavioral details, leaving significant gaps for an AI agent to understand how to use it effectively.
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 100%, with both parameters ('ano' and 'uf') well-documented in the schema. The description mentions these parameters ('estado e ano específicos') but adds no meaning beyond what the schema already provides, such as explaining why these filters are necessary or their impact on results.
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 action ('Lista' - lists) and resource ('eleições suplementares' - supplementary elections) with specific filtering criteria ('em um estado e ano específicos' - in a specific state and year). It distinguishes from obvious siblings like 'listar_eleicoes_ordinarias' by specifying 'suplementares' vs 'ordinarias', though it doesn't explicitly contrast 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?
The description provides no guidance on when to use this tool versus alternatives like 'listar_eleicoes_ordinarias' or 'listar_anos_eleitorais'. It states what it does but offers no context about appropriate use cases, prerequisites, or exclusions.
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 provided, the description carries full burden for behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't mention potential limitations like pagination, rate limits, authentication needs, or what happens with invalid inputs. For a tool with 4 required parameters and no annotation coverage, this is a significant gap.
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, efficient sentence that front-loads the core purpose without any wasted words. It directly communicates what the tool does in a compact form, earning full marks for conciseness.
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 tool's moderate complexity (4 required parameters, no output schema, no annotations), the description is minimally adequate. It clarifies the scope (municipality-specific candidates) but omits details on return format, error handling, or sibling tool relationships, leaving room for improvement in completeness.
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 100%, with each parameter well-documented in the schema (e.g., 'ano' as election year with examples). The description adds no additional parameter semantics beyond implying filtering by municipality and election context, so it meets the baseline for high schema coverage without compensating value.
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 verb ('Lista') and resource ('todos os candidatos') with specific context ('para eleições em um município específico'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'consultar_candidato' or 'listar_cargos_municipio', which prevents a perfect score.
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 like 'consultar_candidato' (which might fetch individual candidate details) or other list tools. It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.
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?
No annotations are provided, so the description carries full burden. It states the tool lists elections for consultation, implying a read-only operation, but doesn't disclose behavioral traits such as pagination, rate limits, authentication needs, or what 'disponíveis para consulta' entails (e.g., availability constraints). The description is minimal and lacks critical operational details.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for its simplicity.
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 no annotations, no output schema, and a simple tool with zero parameters, the description is incomplete. It lacks details on return values (e.g., format, fields), behavioral aspects like error handling or data freshness, and doesn't leverage the opportunity to clarify usage relative to siblings. For a list operation, more context on output and constraints is needed.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. Baseline is 4 for zero parameters, as the description doesn't need to compensate for any schema gaps.
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 action ('Lista todas') and resource ('eleições ordinárias'), making the purpose understandable. It distinguishes from siblings like 'listar_eleicoes_suplementares' by specifying 'ordinárias' but doesn't explicitly contrast them. The description is specific but lacks explicit sibling differentiation.
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 like 'listar_anos_eleitorais' or 'listar_eleicoes_suplementares'. The description implies usage for listing ordinary elections but offers no context on prerequisites, exclusions, or comparative scenarios with sibling tools.
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?
No annotations are provided, so the description carries the full burden. It states it's a list operation, implying read-only behavior, but doesn't disclose any behavioral traits such as rate limits, authentication needs, or what 'disponíveis' entails (e.g., cached data, real-time). For a tool with zero annotation coverage, this is a significant gap.
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, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, with zero waste.
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 tool's simplicity (0 parameters, no output schema), the description is adequate as a basic list operation. However, with no annotations and no output schema, it lacks details on return format (e.g., list structure, data types) and behavioral context, which could be helpful for an agent.
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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, earning a baseline score of 4 for this dimension.
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 verb ('Lista') and resource ('anos eleitorais disponíveis no sistema'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'listar_eleicoes_ordinarias' or 'listar_eleicoes_suplementares', which appear to list specific types of elections rather than years.
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 doesn't mention prerequisites, context for usage, or how it relates to sibling tools that list elections or candidates.
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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