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shanjai-raj

commune-mcp

by shanjai-raj

submit_feedback

Report errors, request missing features, and share quality signals about Commune's API directly to the product team.

Instructions

Submit feedback about Commune to the product team.

Use this tool whenever you encounter something worth reporting — an error, a missing capability, or an observation about what's working well or could be improved.

Three feedback types:

"error" — the API returned a non-2xx status, the response shape was wrong, or behavior was incorrect (not just unexpected). Include context: which tool you called, what you expected, what happened. Example: "list_threads returns 500 when the inbox has never received a message."

"feature" — you need a capability that doesn't exist yet. Be specific about the use case. Example: "Need cursor-based pagination on search_threads — current limit cap blocks processing large backlogs."

"signal" — the API succeeded but quality, latency, or behavior was unexpected. Also use for positive observations. Example: "Semantic search quality on long threads is excellent — intent matching across 40+ messages works." Example: "Thread metadata endpoint is slow (~3s) on inboxes with 1000+ threads — expected <500ms."

The optional context dict lets you attach structured metadata that makes feedback actionable. For errors, include the tool name, any IDs, and status codes. For features, include the related tool and your use case.

Args: type: Feedback type — "error", "feature", or "signal" message: Clear description of the feedback (max 4000 chars) context: Optional structured metadata, e.g. {"tool": "list_threads", "inbox_id": "inb_123", "status_code": 500}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes
contextNo
messageYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It thoroughly explains the submission behavior: what each feedback type means, what content to include in the message, and how to use the context dict. It even gives examples of good and bad feedback behavior. The description also states the message length limit (4000 chars), adding transparency.

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 and front-loaded: the purpose and usage appear in the first two sentences. It then uses clear headings for feedback types and an 'Args' section. Despite its length, every sentence is informative, with examples that replace verbose prose. 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?

For a feedback tool with no annotations and zero schema descriptions, this description is exceptionally complete. It covers the tool's purpose, when to use it, how to categorize feedback, what to include in messages, and how to structure context. The output schema handles return values, so those need not be described. The presence of examples further enhances completeness.

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 description coverage is 0%, but the description compensates fully in the 'Args' section. It explains 'type' with its enum values ('error', 'feature', 'signal'), describes 'message' and its max length, and details 'context' with a concrete JSON example. This adds significant semantic meaning beyond the bare schema definition.

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 a clear, specific statement: 'Submit feedback about Commune to the product team.' This uses a specific verb ('submit') and resource ('feedback about Commune'), and it is distinct from all sibling tools (e.g., send_sms, create_domain). It further clarifies purpose by defining three feedback types, eliminating ambiguity.

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 description explicitly states when to use the tool: 'Use this tool whenever you encounter something worth reporting — an error, a missing capability, or an observation.' It provides detailed criteria for each feedback type with concrete examples, giving the agent clear usage guidance. No alternative feedback tools exist among siblings, so no exclusions are needed.

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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