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Sighthesia

interactive-choice-mcp

by Sighthesia
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Python
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0

  • Disambiguation5/5

    The two tools—poll_selection and provide_choice—have clearly distinct purposes: one presents choices to the user, the other retrieves results after a web switch. There is no overlap.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern in snake_case: 'poll_selection' and 'provide_choice', which is clear and predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin for its stated purpose of interactive choices. While the existing tools are comprehensive, the set lacks additional tools for cancellation or session management, making it borderline.

    Completeness4/5

    The tool set covers the core flow of presenting choices and polling results, but is missing a cancellation or abort mechanism, which is a minor gap for an interactive system.

  • Average 3.5/5 across 2 of 2 tools scored. Lowest: 2.4/5.

    See the Tool Scores section below for per-tool breakdowns.

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

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  • 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?

    With no annotations, the description must carry the full behavioral burden. It states 'polls' but does not clarify if it is blocking, how often to call, timeout behavior, or what happens if the session is invalid. No side effects or required permissions are disclosed.

    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 a single sentence, which is concise, but it omits critical information. Efficiency is undercut by lack of substance, so a middle score is appropriate.

    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 single parameter, presence of an output schema (content unknown), and a sibling tool, the description is incomplete. It does not explain the polling mechanism, expected output, or relationship to 'provide_choice'. Major gaps remain for effective agent use.

    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?

    Schema description coverage is 0%, and the description does not explain the 'session_id' parameter beyond its name. The agent gets no context on how to obtain or format this ID, reducing the tool's usability.

    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 specifies the action (polls), the object (result of an interaction session), and a context clue (switched from Terminal to Web). It distinguishes the tool from its sibling 'provide_choice' by focusing on polling rather than providing input. However, it is vague about what 'result' means.

    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 'provide_choice' or any other alternative. There is no mention of prerequisites, conditions for polling, or how the session was initiated.

    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?

    No annotations exist, so description carries full burden. It transparently explains that the tool presents choices to the user, requires specific arguments, and triggers terminal commands if action_status is pending. However, it does not explicitly state whether the tool is non-destructive or has side effects.

    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 clear sections (USAGE RULES, TERMINAL WORKFLOW, Args) and front-loaded purpose. It is slightly verbose but every sentence serves a purpose, effectively communicating complex workflows.

    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 no output schema and missing schema descriptions, the description is comprehensive: it covers tool purpose, usage rules, parameter details, terminal workflow integration, and links to sibling poll_selection, enabling correct agent invocation.

    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?

    With 0% schema description coverage, the description compensates by detailing the prompt structure (summary + decision request), options format (list of dicts with id/description, include recommended), and selection_mode values. It adds significant meaning beyond the schema, though options structure could be more precise.

    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 explicitly states the tool's purpose: 'resolve ambiguities, confirm high-risk actions, or validate task completion.' This is a specific verb-resource combination that clearly distinguishes it from sibling poll_selection, which polls for selections.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Detailed USAGE RULES, FEEDBACK INTEGRATION, and TERMINAL WORKFLOW provide explicit guidance on when and how to use the tool, including scenarios like pending_terminal_launch and integration with poll_selection.

    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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  • Evaluate tool definition quality.

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