APIs similar to a given one ("more like this") by shared tags.
| Name | Required | Description | Default |
|---|---|---|---|
| aid | Yes | ||
| limit | No | ||
| context | No | Optional: 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It does reveal the key algorithm (shared tags) and the scope (APIs), which is transparent about how similarity is computed. However, it does not mention any limitations, sorting, pagination, or what happens when no tags are shared, leaving some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, succinct sentence that front-loads the core action ('APIs similar to a given one') and includes the key mechanism ('by shared tags'). There is no fluff or redundancy; it is optimally concise for the information conveyed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, and the description captures its essence, but with no output schema, it does not specify the return format or structure. It also omits any hint about how results are ordered or whether the 'limit' parameter is respected. For a tool that returns a list, some indication of the response type or usage notes for 'limit' would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only 33% coverage (only 'context' has a description). The description does not explain parameters beyond implying that 'aid' identifies the given API. It does not clarify the 'limit' parameter's meaning or default behavior, and with low schema coverage, the description should compensate but does not. This leaves agents guessing about the optional parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: finding APIs similar to a given one ('more like this') based on shared tags. It specifies the resource (APIs) and the mechanism (shared tags), which distinguishes it from generic search or provider-level tools. However, it does not explicitly differentiate from the sibling tool find_similar_providers, which is a minor gap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need APIs similar to an existing API, which is evident from the phrase 'more like this'. However, it does not explicitly state when to choose this over find_similar_providers or search tools, nor does it mention any prerequisites or exclusions. It provides a general context but lacks explicit routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
