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FojleRabbiRabib

Gemini Bridge

by FojleRabbiRabib
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Python
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Server Quality Checklist

67%
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  • Latest release: v1.3.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: consult_gemini for plain queries, consult_gemini_with_files for queries with file context, and web_search for web-augmented queries. No functional overlap.

    Naming Consistency4/5

    Naming mostly follows a verb_noun pattern with 'consult_gemini' as a base, but 'web_search' deviates by not starting with 'consult'. Overall pattern is still predictable and readable.

    Tool Count5/5

    Three tools is an appropriate scope for a Gemini CLI bridge, covering the essential query modes (plain, with files, web search) without unnecessary bloat.

    Completeness4/5

    The set covers the primary use cases for interacting with Gemini. Missing tools for model listing or configuration, but these are minor gaps for the intended purpose of querying.

  • Average 3.7/5 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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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 must disclose behavioral traits. It mentions forwarding the query verbatim and returning response/error, but does not clarify read-only nature, authentication needs, rate limits, or potential 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise and well-structured with an initial purpose sentence and a clear Args/Returns format. Every sentence is informative with no fluff.

    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?

    While the description covers invocation details and parameter semantics, it lacks contextual completeness for an AI agent: no guidance on when to use this tool vs siblings, no output schema details, and no behavioral context beyond the immediate 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?

    The description includes detailed parameter explanations (query, directory, model, timeout_seconds) and return value, adding meaning beyond the input schema. Schema description coverage is 0% in JSON, but the docstring compensates well.

    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 states 'Send a query directly to the Gemini CLI', clearly indicating the action and resource. However, it does not explicitly distinguish from sibling tools like 'consult_gemini_with_files' or 'web_search', which would be helpful for an AI agent.

    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. The description lacks explicit context such as prerequisites, when-not-to-use, or comparisons with siblings.

    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?

    With no annotations, the description carries the burden. It discloses the best-effort and model-determined web search behavior. However, it does not mention safety, authentication, or other behavioral traits beyond the core functionality.

    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 and well-structured. It front-loads the purpose, includes a note for important caveats, and clearly separates args and returns. Every sentence adds value.

    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 presence of an output schema and the tool's moderate complexity, the description sufficiently covers the tool's behavior and parameters. It could add more detail on when web search is triggered, but overall it is complete enough for an 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.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It provides brief explanations for all four parameters, adding meaning beyond the schema's type and default info. However, explanations are minimal, e.g., 'directory' is not clearly contextualized.

    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 asks Gemini queries with web search context, using a specific verb and resource. It distinguishes from siblings by emphasizing web search capability, which consult_gemini likely lacks.

    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 notes that web search is best-effort and model-determined, providing some usage context. However, it does not explicitly compare with sibling tools or state when to use this vs 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 provided, but description discloses key behaviors: inline mode streams truncated snippets, at_command mode emits @path directives, and returns explanatory error string with warnings. Adequate for a query tool.

    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?

    Description is relatively concise with a clear intro and parameter list; no wasted sentences. Returns line adds value. Could be more front-loaded but still efficient.

    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 6 parameters, no annotations, and no output schema, the description covers behavior, parameters, and return value sufficiently. Minor gap: no explicit mention of usage context beyond mode difference.

    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?

    With 0% schema coverage, description compensates fully by explaining each parameter's purpose and constraints, e.g., directory resolves relative paths, mode describes two options, model lists aliases.

    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?

    Description specifies the verb 'send a query', the resource 'Gemini CLI', and the distinguishing feature 'with file context', clearly differentiating it from the sibling tool 'consult_gemini'.

    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?

    Description explains the two modes ('inline' vs 'at_command') and their behaviors, providing some guidance on how to use the tool but lacks explicit when-to-use versus siblings or when-not-to-use conditions.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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