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financesaur

ollama-web-tools-mcp

by financesaur
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    web_search clearly differs from the fetch tools. web_fetch and cluesift_domain_fetch both fetch pages, but cluesift_domain_fetch has a distinct delivery target, reducing confusion. One or two could be confused but descriptions clarify.

    Naming Consistency4/5

    web_search and web_fetch follow a consistent noun-verb pattern. cluesift_domain_fetch deviates by adding a longer domain prefix, but still ends with a verb and uses consistent snake_case.

    Tool Count4/5

    3 tools is on the lower end but appropriate for a focused web search/fetch server; each tool has a clear purpose, though the server may seem slightly thin.

    Completeness4/5

    The core web operations of search and fetch are covered, plus a specialized fetch-to-target. Minor gaps exist (e.g., no tool for parsing or summarizing pages) but not critical for the primary use case.

  • Average 2.5/5 across 3 of 3 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 is passing

  • 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 to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 carries the full burden of behavioral disclosure. The only behavioral hint is 'best-effort', implying possible delivery failure, but it does not explain error handling, retries, output format, or side effects. This leaves significant transparency gaps.

    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 a single, concise sentence with no redundant wording. It delivers the primary purpose upfront, but its brevity comes at the cost of missing critical details, so it is efficient yet under-specified.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With four required parameters, no output schema, and no annotations, the description is severely incomplete. It fails to explain the full workflow, the role of each parameter, the destination of the fetched page, or how the agent should handle failures. This tool cannot be invoked confidently based on the description alone.

    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?

    The input schema has 0% description coverage, and the tool description does not explain any of the four required parameters (url, artifact_target, request_id, case_id). The description adds no meaning beyond what the schema already shows, which is just types and an enum, leaving the agent without context for request_id and case_id.

    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 clearly states the core action ('Fetch a public page and best-effort deliver it to a fixed ClueSift target'), which distinguishes it from siblings like web_fetch or web_search. However, 'fixed ClueSift target' is ambiguous given the artifact_target parameter, which can be 'cluesift-test' or 'cluesift-prod'.

    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 the sibling tools (web_search, web_fetch). There is no mention of prerequisites, exclusions, or recommended use cases, leaving the agent to infer usage from the name and description alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It only says 'fetch web page content' with no mention of rate limits, authentication, output format, or how truncation/max_chars affect behavior.

    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 concise sentence, but it is under-specified. It keeps the core action clear but omits necessary operational details, so it is not fully effective conciseness.

    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?

    This is a simple fetch tool with 3 parameters and no output schema or annotations. The description does not cover truncation behavior, character limits, return types, or use cases, leaving a gap in operational understanding.

    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 any of the three parameters (url, truncate, max_chars). The description adds no value over the bare schema, and the agent gets no hints about parameter meanings.

    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 clearly states the tool fetches web page content and identifies the specific API (Ollama's web fetch API). It distinguishes from sibling web_search by focusing on fetching content rather than searching, though it doesn't explicitly compare to cluesift_domain_fetch.

    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 about when to use this tool versus alternatives like web_search or cluesift_domain_fetch. It does not mention any prerequisites, contexts, or exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations, the description must disclose behavior, but it only says 'Search the internet using Ollama's web search API.' It does not mention result truncation, character limits, maximum results, pagination, or API-specific constraints, leaving the agent without critical behavioral knowledge.

    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 a single clear sentence that is front-loaded and free of extraneous text. However, it under-specifies the tool's behavior, balancing conciseness against completeness.

    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 no output schema, no annotations, and four parameters, the description is critically incomplete. It fails to explain return values, parameter use, or tool-specific behaviors, leaving the agent to make unsupported assumptions.

    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?

    The input schema has four parameters with 0% description coverage. The description does not explain the meaning of query, truncate, max_chars, or max_results, relying on schema names alone, which forces the agent to guess semantics.

    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 'Search the internet using Ollama's web search API,' which clearly identifies the action (search), resource (internet), and implementation (Ollama's API). This distinguishes it from sibling tools like web_fetch, though it doesn't explicitly compare against them.

    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 about when to use web_search versus web_fetch or cluesift_domain_fetch. The description lacks any contextual signals, alternatives, or exclusions, scoring a 2 for absent usage guidelines.

    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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