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readiness_gates

INFLUENCE — what blocks the next agent-readiness band, GATES BEFORE POINTS. Some bands are unreachable on score alone: agent-native requires idempotency AND a stable error envelope regardless of how many points a provider collects. Returns the points gap and the unmet gate requirements separately, each priced, plus a verdict saying whether clearing the gate also closes the gap — the case a points-arithmetic answer gets wrong every time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
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.

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the burden of explaining behavior. It does this well by disclosing that the tool returns gap and gate requirements separately, that they are priced, and that it provides a verdict about whether clearing the gate closes the gap. This gives an agent a clear picture of the output behavior.

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 dense but purposeful, with the core concept front-loaded ('GATES BEFORE POINTS') followed by concrete return details. Slightly verbose, but every phrase adds relevant information.

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?

The description covers the return shape well and explains the key gate-vs-points distinction. However, with no output schema and no explanation of the slug parameter, an agent may not know how to invoke the tool correctly or fully interpret 'priced' without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50%, and while the context parameter has a thorough schema description, the required slug parameter has no description. The tool description does not clarify what slug refers to, leaving the most important parameter 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 description identifies a specific verb and resource: it returns the points gap and unmet gate requirements that block the next agent-readiness band. It clearly distinguishes itself from points-only readiness reasoning by emphasizing 'GATES BEFORE POINTS' and the specific agent-native gate requirements.

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 clear context for when to use this tool: when determining whether a readiness band is blocked by gates rather than points, especially because some bands are unreachable on score alone. It does not explicitly name alternatives or exclusions, but the intended use case is evident.

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.

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