lobbywatch-mcp

Server Quality Checklist

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Latest release: v0.3.1
Disambiguation4/5
Most tools have distinct purposes (dump management, parliamentarian lookups, ranking, transparency, search). However, `lobbywatch_get_parlamentarier` and `lobbywatch_list_interessenbindungen` partially overlap since the former returns full profiles including interests, but the latter offers filtering options that reduce ambiguity.
Naming Consistency5/5
All tools follow a consistent `<verb>_<noun>` pattern with snake_case and the `lobbywatch_` prefix. Verbs like `get`, `list`, `refresh`, and `search` clearly indicate the action, and the naming is uniform throughout.
Tool Count5/5
With 8 tools, the set is well-scoped for the lobbywatch domain. Each tool covers an essential aspect (dump status/refresh, parliamentarian/lobby group lookups, ranking, transparency, interest listing, branch search) without bloat or insufficiency.
Completeness3/5
The tools cover core operations but miss basic listing functionality (e.g., no `list_parlamentarier` or `list_lobbygruppen`). Users cannot get an unfiltered list of all parliamentarians or lobby groups, which limits general discovery. Other CRUD operations are absent but presumably out of scope.
Average 4.4/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 39 commits in the last 12 weeks
- Last stable release on August 2, 2026
- 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.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description adds value by stating it aggregates and returns a distribution. No contradiction. The description provides sufficient behavioral context for a read-only, idempotent 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 concise with a summary sentence and three bullet-point use cases. Every sentence adds value and the structure is front-loaded with the core action.
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?
Given the tool has one optional parameter and an output schema, the description covers the main purpose and usage scenarios adequately. It does not discuss limits or edge cases but is complete for a simple aggregation tool.
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 input schema has 0% description coverage, but the description mentions commission filtering and uses cases reference specific commissions. However, it does not explicitly describe the parameter name or format, leaving some ambiguity despite the context.
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 aggregates 'verguetungstransparenz_beurteilung' values across parliamentarians or a commission subset and returns the distribution. It provides specific verb 'Aggregate' and resource, and the use cases differentiate it from siblings like 'lobbywatch_get_ranking'.
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 gives explicit use cases (e.g., 'How transparent is the FK-N on compensation disclosure?') and implies when to use, but does not explicitly state when not to use or mention alternatives among sibling tools.
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?
Annotations already provide readOnlyHint=true and idempotentHint=true, so the description adds no new behavioral details beyond reaffirming no side effects. No contradiction; bar is lower with annotations present.
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 extremely concise: two lines plus bulleted use cases. Every sentence earns its place; no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, rich annotations, and an output schema present, the description is fully sufficient. It covers purpose, when to use, and no missing info.
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 zero parameters, and schema coverage is 100%. The description adds no parameter details, but none are needed. Baseline of 4 is appropriate for this case.
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 'Return current dump cache status without forcing a refresh,' specifying the action and the resource. Use cases further clarify the tool's purpose, and it distinguishes itself from sibling tools like lobbywatch_refresh_dump.
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?
Explicit use cases are provided, indicating when to check freshness vs. refresh. While it doesn't explicitly state when NOT to use, the contrast with refresh_dump is clear enough for an AI agent to differentiate.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true; description aligns by describing a read-only ranking. No additional behavioral traits (e.g., rate limits, data freshness) are disclosed beyond what annotations provide, but no contradictions.
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?
Extremely concise: single sentence intro, clear Args section, and two-line Use cases. Every sentence adds value; no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, no need to describe return values. All parameters are covered, use cases illustrate typical applications, and the tool's purpose is fully contextualized for an AI 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?
Schema description coverage is 0%, so description compensates by explaining each parameter: enum values for kriterium (with examples), optional filters for kommission and partei (with sample values), and range for limit. Provides meaning beyond raw schema.
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 'Rank parliamentarians by a criterion', specifying the verb and resource. It distinguishes from sibling tools like lobbywatch_get_parlamentarier or lobbywatch_list_interessenbindungen which do not perform ranking.
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?
Provides concrete use cases (e.g., 'Top 10 SP MPs by total mandate count') and explains parameters like kriterium, kommission, partei, limit. Lacks explicit when-not-to-use or alternatives, but examples strongly imply appropriate contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds transparency by noting the live REST API and fresh data, plus the inclusion of connected entities. No contradictions, but more detail on rate limits or auth could further enhance.
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 concise with a clear purpose statement followed by illustrative use cases. Every sentence adds value, and the structure is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately covers what the tool does, including its return content (connected organisations and parliamentarians). With an output schema available, further detail on return format is unnecessary.
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 no parameter descriptions (0% coverage), but the description compensates by showing examples of using a name or ID, clarifying the parameter's purpose and format.
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 fetches a lobby group including connected organisations and parliamentarians. It is specific and distinguishes from sibling tools that focus on individual parliamentarians, rankings, or transparency quotes.
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 example use cases that illustrate common queries, giving clear context for when to use the tool. However, it lacks explicit guidance on when not to use it or alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), the description adds key behaviors: fuzzy matching with suggestions on near-miss, and optional restrictions (nur_hauptberuflich, nur_aktiv). It does not contradict annotations.
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 well-structured with sections for purpose, behavior, Args, and use cases. It is slightly verbose but each sentence adds value, and it is front-loaded with the main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists and the parameters are simple, the description covers all necessary aspects: input parameters, fuzzy matching, optional filters, and common use cases. It does not require further elaboration.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains each parameter clearly in the Args section (e.g., 'nur_hauptberuflich: If True, only main-occupation mandates'), compensating for the 0% schema description coverage. Use cases further illustrate parameter usage.
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 'Return the list of interessenbindungen (conflicts of interest) for one parliamentarian', using a specific verb and resource. It distinguishes from sibling tools like 'lobbywatch_get_parlamentarier' by focusing on conflicts of interest.
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 three concrete use cases (e.g., 'Which active mandates does Jositsch hold today?') that illustrate when to use the tool. However, it does not explicitly mention when not to use it or name alternatives, though the sibling tools cover different purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent. The description adds specific behavioral details such as substring matching, case-insensitivity, and version 0.1 limitations, which go beyond the annotations.
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?
Well-structured with purpose, limitations, Args, and Use cases. While informative, it could be slightly more concise; the version note and future improvements add length but are still relevant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema, the description adequately covers input semantics, use cases, and behavioral nuances. No critical gaps remain for agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but the description fully explains all three parameters: branche_query (matching fields), kommission (commission abbreviation), and limit (range 1-200). It compensates completely for the missing 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 it finds parliamentarians based on a search term against organisation fields, with optional commission filter. It distinguishes from siblings like 'lobbywatch_get_parlamentarier' by focusing on industry/commission search.
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?
Provides clear use cases (e.g., 'Which WBK-N members hold mandates in publishing?') and explains limitations like substring matching. However, it does not explicitly mention when not to use or name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds value beyond annotations by describing fuzzy miss handling and that the profile includes interessenbindungen. No contradictions.
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?
Well-structured with summary, args, use cases, and note. Every sentence adds value; no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter, the description covers all relevant aspects: behavior on hit/miss, parameter explanation, and use cases. Output schema exists but is not needed in description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Explains that name_or_id can be a numeric ID or fuzzy name, and that partial last names work. Schema coverage is 0%, so the description fully compensates.
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 states it 'Look up a Swiss federal parliamentarian and return their full profile', clearly identifying the resource and action. It also distinguishes from sibling tools like lobbywatch_search_parlamentarier_nach_branche.
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?
Provides explicit use cases ('Show me Anna Mustermann's full lobbying profile') and explains fuzzy miss behavior. Does not explicitly contrast with siblings but offers sufficient guidance.
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?
The description discloses behavioral traits beyond annotations: it reports progress via MCP Context (audit SDK-003) during long-running downloads (~17 MB compressed). This provides valuable information about execution time and user feedback. Annotations already indicate non-read-only, non-destructive, idempotent, and open-world hint, and the description complements these without contradiction.
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 concise, with the first sentence stating the primary action and return value. The following sentences add important context about progress reporting and use cases. Every sentence is meaningful, and the structure is front-loaded with the core purpose.
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?
The description covers the purpose, behavior, and usage context adequately. It mentions the return value ('new cache status') and progress reporting. Given that no parameters are needed and an output schema exists, the description is complete enough for an agent to invoke and interpret the result. Minor improvement could be a brief note on what 'cache status' entails, but the output schema likely handles that.
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 zero parameters, and the input schema is empty with 100% coverage. According to guidelines, 0 parameters yields a baseline of 4. The description does not need to add parameter information, so this score 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 tool forces a re-download of the weekly Lobbywatch dump and returns the new cache status. It uses specific verbs (force re-download, returns) and resource (Lobbywatch dump). It distinguishes from siblings by implying this is for forcing a refresh, while siblings like lobbywatch_dump_status are likely for status checks without triggering a download.
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 explicit use cases ('Force a fresh download — the data looks stale' and 'I just heard about a new declaration — refresh and re-check'), which guide the agent on when to use the tool. It does not explicitly state when not to use it or mention alternatives, but the presence of sibling tools and the context of 'force re-download' vs. status check is implied. Slightly lacking in explicit exclusion guidance.
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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