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report_correction

Report that the catalog has a provider WRONG — a wrong website, a wrong logo, APIs that are not theirs, a count that does not match what they ship, a score computed from any of those. Free, always: correcting our own error is never a paid feature. Read by a human. If you are the provider, say so — it does not gate anything, it tells us how fast to move.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe provider slug the record is filed under.
fieldNoOptional field name: website, image, api_count, tags, score, access_model, apis.
wrongYesWhat is incorrect. Name the field if you can.
contextNoOptional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable.
correctNoWhat it should say instead.
evidenceNoA URL that shows it — your own docs, your own site. This is what makes a correction actionable rather than a claim.
relationshipNoYour relationship to the provider. Never gates the report; it sets priority.

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it is always free, read by a human, and the provider relationship affects priority but not acceptance. It also clarifies that being a provider does not gate the report. These are non-obvious and valuable. It does not describe any side effects (e.g., whether a confirmation is returned), but for a report tool, this is acceptable.

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 three sentences and front-loads the purpose. It includes necessary caveats (free, human-read, provider status) without excessive verbosity. It is concise enough for an agent to parse quickly, though it could be slightly tighter by removing redundant phrases like 'Free, always' which is also implied.

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 reporting tool with 7 parameters and no output schema, the description covers the essential aspects: what constitutes an error, that it is free, human-reviewed, and that provider status influences speed. It does not mention what happens after submission (e.g., no immediate response), but 'read by a human' implies that. It adequately complements the schema for an agent to decide and invoke 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 100%, so the baseline is 3. The description reinforces the relationship parameter ('If you are the provider, say so') and encourages naming the field in 'wrong', but it does not add substantial new meaning beyond the schema. The schema already provides thorough descriptions for each parameter, so the tool description adds marginal value here.

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's purpose: to report a catalog error about a provider, with specific examples (wrong website, logo, APIs, count, score). It distinguishes itself from sibling tools like find_providers and get_provider by being a reporting action rather than a query, and it's distinct from submit_feedback by focusing on catalog corrections. The verb 'report' and resource 'correction' are explicit.

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 implies when to use it (when you find incorrect provider data) but does not explicitly contrast with alternatives like submit_feedback or other reporting tools. It provides some usage context by stating that provider status affects speed but not gating, and that it is always free. It could have named an alternative for general feedback, but the purpose is clear enough.

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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TDQS

B3.1/5.0
Disambiguation3/5

Most tools are clearly separated by artifact type or resource (find_mcp vs find_openapi vs get_provider vs get_api), but the sheer volume creates some genuinely confusable clusters: apis_io_search vs find_apis vs find_artifacts, and insights_adoption vs insights_dimensions vs find_company_insights. Several readiness-related tools (what_can_i_fix, simulate_fixes, readiness_gates) also share a conceptual boundary, though their descriptions do help.

Naming Consistency3/5

The dominant patterns (find_*, get_*, cohort_*, compare_*) are consistent and predictable, but the set mixes in irregular names like apis_io_search, tag_group_tags, what_can_i_fix, whats_changed, and resolve. These deviations are readable but break the otherwise regular verb_noun convention.

Tool Count2/5

106 tools is far beyond the typical well-scoped server and will impose a heavy selection burden on agents. The server covers a genuinely broad domain (catalog search, ratings, cohorts, agent readiness, lists, exports, feedback), so the count is defensible in scope, but it is still too many to navigate efficiently.

Completeness5/5

The surface is remarkably complete: search and browse, single-entity detail, comparisons, cohort analytics, agent-readiness assessment, saved searches, list management, feedback/correction flows, and full dataset exports are all covered. There are no obvious dead ends, and even minor operations like re-running saved searches or simulating fixes are present.

Resources