political-finance-mcp-server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct aspect of political finance: campaign summary, candidate fundraising, congressional votes, PAC spending, industry donors, and individual donations. There is no overlap in functionality.
Naming Consistency4/5
All tools use lowercase snake_case with a verb_noun pattern. Most start with 'get_', but one uses 'search_', which is a minor inconsistency. Overall, the naming is clear and predictable.
Tool Count5/5
With 6 tools, the set is well-scoped for a political finance domain. Each tool covers a key area without being overwhelming or underwhelming.
Completeness4/5
The tools cover major facets of campaign finance: summary, candidate, PACs, donors, and votes. Minor gaps like independent expenditures or lobbyist data exist but are not critical for basic use.
Average 3.2/5 across 6 of 6 tools scored. Lowest: 2.6/5.
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
This repository is licensed under MIT License.
This repository includes a README.md file.
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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?
No annotations are provided, so the description carries the full burden. It only states that it retrieves PAC spending and income, but fails to disclose behaviors like whether it is read-only, data freshness, or rate limits. The description is minimal.
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 very short and to the point, using a structured format with Args and Returns sections. However, it could be more readable and less terse.
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 output schema, the description vaguely mentions 'PAC spending and income' without detail. It lacks examples, edge case handling, or clarification of the data structure. For a tool with 3 parameters, it is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description lists parameters but adds no meaning beyond their names. With 0% schema description coverage, the description does nothing to clarify expected formats, allowed values, or semantics. Essentially redundant with the input schema.
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 it retrieves PAC spending (Political Action Committees), which is specific and directly related to the tool name. However, it does not differentiate from sibling tools like get_campaign_finance_summary or get_top_donors_by_industry.
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 prerequisites or conditions. The description is purely functional without 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 must fully disclose behavioral traits. It mentions the tool is read-only implicitly by describing returns, but does not state data freshness, potential delays, or any side effects, which is insufficient for a tool with zero annotation coverage.
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 concise, using a standard Args/Returns format. It wastes no words, though the German language may be a minor accessibility concern. Every section is purposeful.
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 simple one-parameter tool with no output schema, the description is moderately complete: it states purpose and return elements. However, it lacks details on the format of the summary (e.g., is it a list, dictionary?) and does not address edge cases, which would improve completeness.
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 only parameter, election_year, is described in the tool's docstring but essentially repeats the schema (default value). With 0% schema description coverage, the description adds no interpretive guidance beyond what the schema already provides, failing to compensate for the gap.
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 it provides an overview of campaign finance for a year, listing specific outputs like total spending and top candidates. However, it does not differentiate from sibling tools such as get_candidate_fundraising or get_pac_spending, missing an opportunity to clarify its unique scope.
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 explicit guidance on when to use this tool versus alternatives. The description implies its use for a yearly summary, but gives no conditions or exclusions, leaving an agent to infer 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 mentions the data source ('via FEC') and return format ('mit Summen'), but does not disclose behavioral traits like rate limits, data freshness, or authorization needs. There is no contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is organized with Args and Returns sections, but uses German which may hinder English agents. It is relatively concise but could be more streamlined and include English translations for broader accessibility.
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?
The description explains parameters and return value (top donors with sums), but lacks details on error handling, pagination, or limitations. Given no output schema, more context would be beneficial for a data tool.
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% description coverage, but the description adds meaning by listing parameters with examples ('technology', 'finance'), defaults (election_cycle=2024, limit=10), and purpose. This compensates for the lack of schema descriptions.
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 shows top donors by industry, using 'Top-Spender nach Industrie/Branche (via FEC).' The verb 'zeigen' (show) and resource 'top donors by industry' are specific. While it distinguishes from siblings like get_campaign_finance_summary, it does not explicitly differentiate in the description.
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 on when to use this tool versus alternatives. The description lacks context on preferred scenarios, exclusions, or comparisons to sibling tools such as search_individual_donations or get_pac_spending.
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 full burden but only states the basic retrieval purpose. It does not disclose behavioral traits like rate limits, authentication, error handling, or whether the operation is read-only.
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 short and efficient: a one-line purpose followed by a concise bullet-like list of arguments and return value. No redundant sentences, and the structure is front-loaded with the key purpose.
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?
The parameter descriptions are adequate, but the return value is only vaguely described as 'voting statistics and recent votes' without an output schema. Missing details about error handling, pagination, or data source limitations reduce completeness for a tool with no output schema.
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?
Despite 0% schema description coverage, the description adds meaningful context: provides examples for member_name ('Sanders', 'Warren'), specifies allowed values for chamber ('senate' or 'house'), and explains the default and meaning of congress (118 = current). This goes beyond the bare schema types.
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 retrieves voting behavior of a congress member via ProPublica. It uses a specific verb ('retrieves') and resource ('voting behavior'), and distinguishes itself from sibling tools focused on campaign finance and donations.
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 or prerequisites. The sibling tools are about finance, but the description does not explicitly recommend this tool for voting records or exclude other contexts.
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 exist, so the description must cover behavioral traits. It implies a read operation and lists return fields, but omits details like error handling, rate limits, or authentication needs. The description is adequate but not rich.
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 very concise with a clear structure: purpose sentence, Args with examples, and Returns. Every sentence adds value with no 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?
For a simple retrieval tool with two parameters and no output schema, the description covers the basics but lacks error context or scope details. It is minimally complete.
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 0%, so the description adds essential meaning: examples for candidate_name ('Biden', 'Trump') and a default for election_year (2024). This compensates well for the lack of 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 the tool retrieves fundraising income and expenses for a candidate, using specific verbs and specifying the resource. It distinguishes from siblings like 'get_pac_spending' or 'get_campaign_finance_summary' by focusing on candidate-level fundraising.
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 does not provide any guidance on when to use this tool versus alternatives. With five sibling tools, explicit usage context is missing.
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 full burden but only states it searches and returns a list. It mentions no side effects, authentication needs, rate limits, or other behavioral traits, leaving the agent uninformed about potential constraints.
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 concise with a short German summary and a structured docstring for arguments and returns. It front-loads the main purpose, though mixing languages slightly reduces clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with three parameters, the description covers purpose, parameters, and return format. It lacks details on pagination or result limits, but overall provides sufficient context for typical use.
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 0%, but the description adds meaning: donor_name can be just last name, min_amount defaults to 200 USD, election_cycle is the cycle. This compensates well for the schema's lack of descriptions, though format of election_cycle could be clearer.
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 it searches for individual donations from a person to election campaigns, using both German and English. This distinguishes it from siblings like get_pac_spending and get_top_donors_by_industry by focusing on individual donors.
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 does not explicitly guide when to use this tool versus alternatives. Usage context is implied by the tool's name and purpose, but no when-not or alternative scenarios are mentioned.
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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