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apis-io

by io.apis

gap_analysis

INFLUENCE — For each provider: high-value artifacts it lacks (MCP, Arazzo, Rules, Skills…), what its peers commonly publish but it lacks, and its score vs the peer median — plus stack-level gaps across the set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
min_shareNoPeer-share threshold for a gap (0–1, default 0.25).
providersYesProvider slugs (1+). Multiple = treat as a stack.

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden, and it does disclose the main output components: lacking artifacts, peer comparisons, score vs peer median, and stack-level gaps. However, it does not state whether the operation is read-only, how the report is returned, or what peer/cohort definition is used, so the transparency is partial.

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 one compact sentence that front-loads the result categories and avoids padding. The leading 'INFLUENCE' label adds little for an agent, but the core content is efficient.

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?

There is no output schema, so the description's list of report components is useful, but it does not specify the response shape, the peer set, or how min_share changes results. Given the number of sibling tools and the analytical nature of this tool, a bit more context about what constitutes a gap would improve completeness.

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 coverage is 100%, so the baseline applies; parameter descriptions already document context, min_share, and providers. The description reinforces the stack notion for providers but does not add new parameter semantics beyond what the 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 identifies the resource (providers) and enumerates concrete outputs: missing high-value artifacts, peer-common artifacts, score vs peer median, and stack-level gaps. It is clear about what the tool computes, though it does not use an explicit verb and does not contrast with closely named siblings like company_gaps or industry_gap_analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to choose gap_analysis over the many overlapping sibling tools, such as compare_providers, company_gaps, or industry_gap_analysis. The context parameter is described, but the description does not state selection criteria or exclusions.

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