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create_list

UNDERSTANDING — Create a named, persistent list (a shortlist / stack you own) of providers or APIs. Requires an apis.io API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
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.
membersNoProvider slugs and/or aids to seed the list.

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose that the list is persistent and that an API key is required, but it does not state what happens on duplicate names, whether it is idempotent, or what the response contains. For a creating/mutating tool this is a notable gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core content is a single clear sentence, but the leading 'UNDERSTANDING — ' prefix adds no functional value and slightly obscures the actionable verb. The essential information is front-loaded, but every word should earn its place and this one does not.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a creation tool with no output schema and no annotations, the description omits important operational context: return behavior, idempotency, error handling, and relationship to sibling tools like add_to_list or get_list. It is minimally sufficient for invoking the tool, but not enough for an agent to reason about consequences or workflows.

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 67%, with 'name' lacking a schema description. The tool description adds marginal context ('named' for name, 'providers or APIs' for members) but does not clarify name format, validation, or how members map to slugs/aids beyond the schema. Overall it adds a little value over the schema without fully compensating.

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 ('Create') and resource ('named, persistent list ... of providers or APIs'), and clarifies the list is a shortlist/stack the user owns. This distinguishes it from siblings like get_list, delete_list, and list_lists, though it does not explicitly name the closest sibling, add_to_list.

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?

The description gives a prerequisite (requires an apis.io API key) but provides no guidance on when to use this tool versus alternatives such as add_to_list, get_list, or delete_list. An agent must infer that create_list is for making a new list, while add_to_list handles additions to an existing one.

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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