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Heretek-RE

re-speakeasy

by Heretek-RE
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct operation: checking setup, running emulation, and listing APIs. There is no overlap in purpose.

    Naming Consistency5/5

    All tool names follow the verb_noun pattern in snake_case (check_speakeasy, emulate_binary, list_emulated_apis), ensuring predictability.

    Tool Count5/5

    With 3 tools, the server is tightly scoped to its core functionality: verifying the environment, performing emulation, and querying capabilities. No unnecessary bloat.

    Completeness5/5

    The tool set covers the full workflow for Speakeasy integration: availability check, binary emulation, and API surface listing. No obvious gaps for the intended use case.

  • Average 4.5/5 across 3 of 3 tools scored.

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

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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  "maintainers": [
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  ]
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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?

    Describes the warning behavior (WARN not ERROR) and the fallback chain for locating the helper, providing good transparency beyond what annotations (none provided) would cover.

    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?

    Concise two-sentence description: first sentence states purpose, second explains behavior and fallback. No unnecessary words, front-loaded key information.

    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?

    Completeness is high for a simple check tool with no parameters and no output schema. Covers return value, behavior on failure, and fallback chain. Missing output format detail but not critical.

    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?

    No parameters exist, so baseline score of 4 applies. Description adds behavioral context beyond schema but does not need to explain parameters.

    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?

    Clearly states the tool returns speakeasy-cli version and Python module availability. Uses specific verb 'return' and resource, distinct from sibling tools like emulation and API listing.

    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?

    Implies usage as a diagnostic check, mentions that WARN is reported instead of ERROR when helper is not found, but lacks explicit when-to-use or when-not-to-use guidance relative to alternatives.

    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 are provided, so the description carries the full burden. It describes the in-process loading, the emulator surface, the trace structure, and the error case for missing helper. It does not mention concurrency, memory, or side effects, but is generally transparent.

    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 a clear summary, explanation of Speakeasy, parameter details, and return format. It is slightly verbose but each sentence serves a 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 the complexity (2 parameters, no output schema), the description is highly complete. It explains the tool's operation, parameter behavior, return structure with an example, and error handling, leaving no major 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%, so the description must add meaning. It clearly explains 'path' as Windows .exe/.dll to emulate and 'timeout_s' as wall-clock budget with a warning about hanging, providing crucial context beyond the 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 specifies the tool runs a binary under Speakeasy and returns a structured per-API trace. It distinguishes itself from siblings (check_speakeasy, list_emulated_apis) by focusing on execution and trace generation.

    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 implies when to use this tool (to emulate a binary and get a trace) and mentions the timeout default and potential hanging, but does not explicitly state when not to use it or compare with 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?

    No annotations provided, so description must fully disclose behavior. It explains the default output is not the full list but a count and sample categories, and mentions the list is large. This is sufficient for understanding the tool's behavior.

    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?

    Two well-structured sentences. First states purpose, second provides usage guidance and output details. No filler.

    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 purpose, usage, and default output. It does not explicitly describe the output format or whether a full list can be obtained, but for a simple listing tool with no parameters, this is largely complete.

    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?

    No parameters in schema, but description adds crucial context about default output behavior (count and sample categories) and scale (thousands of APIs). This goes beyond the 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 it returns the list of Win32 APIs Speakeasy can emulate, using a specific verb ('Return') and resource ('list of Win32 APIs'). It differentiates from siblings by positioning itself as a pre-check before emulation, unlike 'emulate_binary' which actually emulates, and 'check_speakeasy' which is likely a different check.

    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?

    Explicitly states when to use: 'Useful for "can Speakeasy handle this binary's API surface?" before calling emulate_binary on a long-running target.' This provides clear context and alternatives.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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