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matthewhand

mcp-flowise

by matthewhand
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one lists available chatflows, and the other creates predictions using a specific chatflow. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (list_chatflows, create_prediction) with clear, descriptive names that align with their functions. No deviations or mixed conventions are present.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent domain of interacting with Flowise chatflows. While the tools cover basic operations, the lack of tools for updating, deleting, or managing chatflows suggests an incomplete surface that may limit agent workflows.

    Completeness2/5

    The toolset is severely incomplete for a chatflow management domain. It provides listing and prediction creation but lacks essential CRUD operations like creating, updating, or deleting chatflows, as well as tools for managing predictions (e.g., retrieving or canceling them), which will likely cause agent failures in complex tasks.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses that this is a creation/mutation tool ('Create a prediction') and mentions the API source ('Flowise API'), but lacks details about authentication needs, rate limits, error handling beyond 'error message', or whether predictions are stored persistently.

    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 appropriately sized with clear sections (purpose, args, returns). The first sentence states the core purpose, and subsequent details are necessary. Minor improvement could be merging the first two sentences for better flow.

    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?

    Given 2 parameters with 0% schema coverage and no output schema, the description provides basic parameter semantics and return type ('raw JSON response' or 'error message'), but lacks details on response structure, error cases, or integration context (e.g., what a 'prediction' entails in this system).

    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?

    Schema description coverage is 0%, so the description must compensate. It adds meaningful context for both parameters: chatflow_id is optional with a default value from environment, and question is the prompt to send. However, it doesn't explain format constraints or provide examples.

    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's purpose: 'Create a prediction by sending a question to a specific chatflow or assistant.' It specifies the verb ('Create a prediction') and resource ('chatflow or assistant'), but doesn't explicitly differentiate from the sibling tool 'list_chatflows' beyond their different functions.

    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 implies usage context by mentioning 'chatflow or assistant' and referencing 'FLOWISE_CHATFLOW_ID' as a default, but doesn't provide explicit guidance on when to use this tool versus alternatives or any prerequisites. The sibling tool 'list_chatflows' is mentioned but not compared.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a read operation (implied by 'List'), respects configuration-based filtering, and returns JSON-encoded data. However, it doesn't mention potential rate limits, authentication needs, or error handling, leaving some gaps in behavioral context.

    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 perfectly concise and well-structured: three sentences with zero waste. The first sentence states the purpose, the second explains configuration behavior, and the third specifies the return format. Every sentence earns its place and information is appropriately front-loaded.

    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 tool's simplicity (0 parameters, no output schema, no annotations), the description provides good contextual completeness. It covers purpose, behavioral constraints (filtering), and return format. However, without annotations or output schema, it could benefit from more detail about the structure of returned JSON or error conditions for a fully complete picture.

    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 input schema has 0 parameters with 100% coverage, so the description doesn't need to compensate for parameter documentation. The description appropriately focuses on behavioral aspects rather than parameter semantics, which is correct for a parameterless tool. It adds value by explaining the filtering behavior beyond what the empty schema provides.

    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's purpose: 'List all available chatflows from the Flowise API.' This specifies the verb ('List') and resource ('chatflows'), though it doesn't explicitly differentiate from its sibling tool 'create_prediction' beyond the obvious action difference. The purpose is clear but lacks explicit sibling comparison.

    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 implies usage context by mentioning whitelisting/blacklisting configuration, but it doesn't provide explicit guidance on when to use this tool versus alternatives. There's no mention of when not to use it or direct comparison to 'create_prediction', leaving usage context somewhat implied rather than clearly articulated.

    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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