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save_search

UNDERSTANDING — Persist the current search criteria as a named saved search you own, so you can re-run it and get net-new results later. Criteria use the same filters as apis_io_search. Requires an apis.io API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree text over name + description.
areaNo
bandNoRating bands: exemplar, strong, developing, thin, minimal.
nameYes
tagsNoTag slugs.
matchNoany
regionNo
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.
industryNo
min_scoreNo
artifact_typesNo

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does add meaningful context: the saved search is owned by the user, it enables net-new results later, it requires an apis.io API key, and it reuses apis_io_search filters. However, it does not disclose overwrite behavior on duplicate names, whether this is a creating or updating operation, or any persistence/visibility details beyond ownership.

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 compact and front-loaded: the first clause gives the core action, followed by the purpose and the API key requirement. Every sentence contributes useful information, and there is no fluff. The 'UNDERSTANDING —' prefix is slightly unconventional but does not detract.

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?

For an 11-parameter save operation with no annotations and no output schema, the description gives a clear high-level purpose and points to apis_io_search for filter semantics. But it omits what a successful save returns, what happens when a saved search with the same name already exists, and whether criteria are validated at save time. These gaps make it adequate but not comprehensive.

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 only 36%, so the description must compensate. It does so partially by stating 'Criteria use the same filters as apis_io_search', which points the agent to a sibling tool's semantics. The saved search name parameter is implied by 'named saved search', but individual fields like min_score, context, and q are not explained beyond the schema's sparse coverage.

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 states a specific verb ('Persist') and resource ('named saved search you own') and explains the purpose ('so you can re-run it and get net-new results later'). It clearly distinguishes saving from running or listing saved searches, though it does not explicitly name a sibling tool to differentiate from.

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

Usage Guidelines3/5

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

The description gives useful context: criteria use the same filters as apis_io_search and an API key is required. However, it does not explicitly state when to use this tool over alternatives like run_saved_search or list_saved_searches, nor does it provide exclusions; usage is implied rather than stated.

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