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spranab

icantmarket-mcp

by spranab

submit_review

Submit a substantive review on an open ask; the review is scored for quality and flagged for admin review if it contains hype or duplicates.

Instructions

Submit a substantive review on someone else's open ask. Requires ICANTMARKET_API_TOKEN. Cannot self-review own asks. Body is scored for hype-words, content-fingerprint similarity against the reviewer's prior reviews, profile-match, sock-puppet clustering, and AI-content detection. Flags don't block; they surface on the admin review queue. The recipient sets the Helpful/Completed verdict later — that's what credits the reviewer's Review-to-Post balance. Use list_asks to find an ask UUID first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesSubstantive prose. Pasted boilerplate, low-similarity content, and AI-detected text get flagged.
ask_idYesUUID of the ask you're reviewing. Get from list_asks.
hype_acknowledgedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does exceptionally well. It discloses the scoring criteria, that flags do not block submission, that flagged reviews go to the admin queue, and that credit is applied only after the recipient sets the verdict.

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 dense but every sentence adds needed context: purpose, auth, exclusion, scoring behavior, outcome timing, and how to get the required ID. It front-loads the primary action and has 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?

For a submission tool with no output schema, the description covers prerequisites, constraints, moderation behavior, and the credit flow well. The main remaining gap is the unexplained hype_acknowledged parameter and the lack of any return-value hint, but neither prevents a competent agent from selecting and invoking the tool.

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?

The description enriches body semantics by explaining what gets flagged, and it reinforces the ask_id requirement via list_asks. However, schema coverage is 67% and the hype_acknowledged parameter remains undocumented in both the schema and the description, leaving its meaning ambiguous.

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 opening verb-resource pair, 'Submit a substantive review on someone else's open ask,' is specific and immediately distinguishes this from siblings like post_ask and list_asks. It also states the core constraint that the review must target another user's ask.

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 gives explicit conditions: requires ICANTMARKET_API_TOKEN, cannot self-review own asks, and should be preceded by list_asks to obtain an ask UUID. It does not directly compare against post_ask as an alternative, but the usage context is clear enough to route an agent correctly.

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