RocketList

by ai.rocketlist

Overview Discussions Admin

Search hiring companies

search_companies

Read-onlyIdempotent

Search public Rocketlist hiring companies. Returns bounded, non-personal company data only.

Only companies with at least one active job are returned, matching the company list on rocketlist.ai.

All list arguments are arrays of strings; passing a bare string is a validation error.

Filters:

query: substring of the company name or slug, or a whole sector word matching the company's industry, vertical or subvertical. 'fintech' finds companies whose industry is FinTech; 'acme' finds Acme Robotics by name. Sector matching is by whole word, so 'ai' matches the AI & ML vertical and not Blockchain.
investor: companies backed by these investors, e.g. ['Index Ventures']. Common aliases resolve ('a16z' finds Andreessen Horowitz). Populated for about 83% of hiring companies, so a company with no recorded investors is never returned when this is set.
stage: exact company funding stage from ['Pre-Seed', 'Seed', 'Series A', 'Series B', 'Series C', 'Series D', 'Series D+', 'Series E', 'Series F', 'Series G', 'Growth', 'Public', 'Acquired', 'Bootstrapped'], case-insensitive. About 12% of hiring companies have no stage recorded and are excluded when this is set.
yc_batch: Y Combinator batches, e.g. ['F26'] or ['Fall 2026'] (W/X/S/F + year). Covers the batches in the reviewed cohort list (currently YC Fall 2026).
founder_origin: countries the founders come from (where they grew up or studied), by name or ISO code, e.g. ['Germany'] or ['DE']. Company-level, reviewed for the same cohorts as yc_batch; a company matches if any founder does.
near: a place, as an address, a street corner ('3rd and Folsom, San Francisco') or 'lat,lng'. Keeps companies whose office (the reviewed street address on getedge.cc/map) is within radius_m metres (default 1000) or walk_minutes on foot (64 m of straight line per minute). Results carry office_address, distance_m and walk_minutes, nearest first.
radius_m / walk_minutes: size of the near area; give one.

Input Schema

Name	Required	Description	Default
`near`	No
`limit`	No
`query`	No
`stage`	No
`investor`	No
`radius_m`	No
`yc_batch`	No
`walk_minutes`	No
`founder_origin`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedOct 23, 2026

addedInput schema / properties / founder_origin

Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Founder Origin"
+}

addedInput schema / properties / near

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Near"
+}

addedInput schema / properties / radius_m

Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Radius M"
+}

addedInput schema / properties / walk_minutes

Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Walk Minutes"
+}

addedInput schema / properties / yc_batch

Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Yc Batch"
+}

First observedSep 21, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already cover read-only/idempotent/non-destructive, so the description is free to add what they can't: data is bounded and non-personal, only active-job companies are included, coverage is partial (~83% investor data, ~12% missing stage), and matching semantics are disclosed (whole-word sector matching so 'ai' doesn't hit Blockchain). These are exactly the behaviors that change how an agent interprets an empty or partial result set.

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?

Front-loaded with scope and the active-job constraint, then a clean bulleted filter breakdown where each bullet earns its place. It is long, but proportionate to nine undocumented parameters; the only slight waste is the duplicated 'whole word' sector explanation.

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

Completeness5/5

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

An output schema exists, so explaining return values isn't required; the description instead notes the extra fields near-queries add (office_address, distance_m, walk_minutes) and the nearest-first ordering. With nine params at 0% schema coverage, this level of detail is what makes the tool callable correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries the full burden and does so with concrete examples and edge cases: 'fintech' vs 'acme', alias resolution for a16z, the enumerated stage vocabulary, YC batch formats ('F26' or 'Fall 2026'), ISO codes for founder_origin, and three accepted forms for near. It even flags the array-of-strings validation error and the radius_m/walk_minutes either-or rule.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Search public Rocketlist hiring companies") plus the defining scope constraint: only companies with at least one active job, matching the rocketlist.ai list. This clearly separates it from search_jobs (job records) and get_company (single-entity lookup) without needing to name them.

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

Usage Guidelines4/5

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

The filter-by-filter semantics imply when each filter applies, and the exclusion caveats (no investors recorded = never returned; no stage = excluded) tell the agent when results will be sparse. What's missing is explicit routing guidance versus siblings, e.g. "use get_company for full detail" or "use search_jobs to find roles."

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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