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find_company_insights

Browse profiled companies (Fortune 1000) by name; ranked by overall technology-readiness signal. Understanding plan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNo
pageNo
limitNo
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

C2.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It usefully adds that the tool browses Fortune 1000 companies, filters by name, and ranks by a technology-readiness signal. However, it does not describe what a result contains, whether pagination matters, or what side effects (if any) exist. The 'Understanding plan.' fragment is opaque and adds no behavioral clarity.

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 main sentence is compact and front-loaded, stating the core action, scope, and ordering. However, 'Understanding plan.' is an unhelpful appended fragment that wastes the second sentence and creates confusion. Overall it is concise but not clean.

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?

Given no annotations, no output schema, low parameter coverage, and a large sibling family, the description is too thin for an agent to reliably choose and invoke this tool. It omits what an 'insight' result looks like, how to use pagination, and how this compares to get_company_insight. A few more sentences about return shape and selection criteria would be needed.

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 25%, so the description must compensate. It does help infer that q is a company name ('by name') and hints at sorting, but page, limit, and search format are not explained. The context parameter is already described in the schema, so the description adds little new parameter meaning.

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 names a specific action ('browse'), a clear resource ('profiled companies (Fortune 1000)'), and the ranking basis ('overall technology-readiness signal'). It is reasonably distinct from the singular sibling get_company_insight, though the distinction is implicit rather than explicit. The trailing 'Understanding plan.' is confusing but not enough to sink the purpose.

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?

No guidance is given about when to use this tool versus alternatives such as get_company_insight or other find_* tools. The 'by name' phrasing weakly implies a name-based search, but there is no explicit when-to-use, when-not-to-use, or alternative selection criteria.

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