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sebastienfi

MCP Vapi Caller

by sebastienfi
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have entirely distinct purposes: one initiates a call, the other retrieves its result. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow the verb_noun pattern: make_call and get_call_result. The naming is consistent and predictable.

    Tool Count3/5

    With only two tools, the set feels thin but the scope is narrow. The two tools cover the primary workflow, though additional utilities (like cancelling a call) could be expected.

    Completeness4/5

    The core lifecycle of initiating a call and retrieving its result is well covered. A minor gap exists in lack of cancellation or management of calls, but agents can work around this by waiting for completion.

  • Average 5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries full behavioral burden. It discloses async behavior (wait 1-3 minutes), indicates what happens when optional params are omitted (auto-generated first_message, no structured fields without a schema), specifies defaults (language='fr', voice_id from env), and notes the call duration. This is rich behavioral detail that helps the agent anticipate outcomes.

    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-organized with clear sections and front-loads the main purpose. However, the final 'Args' block redundantly repeats parameter descriptions already covered in the REQUIRED/RECOMMENDED/OPTIONAL sections. The TIPS section adds value but also lengthens the text. Overall structured, but slightly overlong due to 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 tool's complexity (13 params, 0% schema coverage, no annotations), the description is remarkably complete. It covers all parameters, workflow, timing, tool dependencies, and best-practice tips. The existence of an output schema reduces the need to detail return values, and the description still notes the call_id response. No critical gaps.

    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 coverage is 0%, but the description compensates exhaustively: it groups parameters into required/recommended/optional, provides format examples (E.164, BCP-47, JSON Schema string), explains each parameter's purpose (e.g., call_goal for automatic success evaluation), and includes usage examples for call_script and first_message. This is far beyond what the schema offers.

    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 opens with 'Make an outbound phone call with an AI voice agent via Vapi', clearly stating a specific verb, resource, and service. It differentiates itself from the sibling tool 'get_call_result' by framing it as the initiating step that returns a call_id.

    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?

    The WORKFLOW section explicitly lays out when to call make_call (step 1) and when to call get_call_result (step 3), including a recommended wait time. This provides clear usage context and names the alternative tool for result retrieval, satisfying the 'when/when-not/alternatives' criterion.

    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?

    No annotations are provided, but the description fully discloses polling behavior, timeout, and the conditional return of fields. It shows exactly what is returned when the call has ended versus in progress, adding rich behavioral context beyond the schema.

    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 well-structured with sections (USAGE, TYPICAL CALL DURATION, RETURNED FIELDS, Args). It is detailed but every sentence contributes, with no redundancy or 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?

    Covers all key aspects: status check, waiting, return fields, polling behavior, and conditional responses. Despite having an output schema, the description adds essential context about the tool's runtime behavior, making it complete for an agent to invoke 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?

    The input schema has no property descriptions (0% coverage), but the description explains both parameters thoroughly: call_id as the ID returned by make_call, and wait_seconds with its polling semantics and default behavior. This compensates fully for the schema gap.

    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 opens with 'Check the outcome of a call initiated by make_call()', which is a specific verb+resource statement. It clearly distinguishes from the sibling make_call tool by focusing on result retrieval rather than initiation.

    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?

    Provides explicit usage patterns: quick status check with call_id only, or waiting via wait_seconds=180 for typical 1-3 minute calls. Explains polling interval (5 seconds) and timeout behavior, plus what to do if the call is still in progress.

    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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