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apollo_bulk_match

Match UP TO 10 people in one call — the way a list actually gets built.

apollo_search_people returns hundreds of people with obfuscated last names and no email. This is how you resolve them: 10 per call instead of one, so 300 people cost 30 calls, not 300.

⚠️ A LOT DOES NOT SAVE CREDITS — Apollo bills PER PERSON, exactly as if you had called apollo_match_person ten times. What it saves is calls, and the rate limit that comes with them. Ask only for the reveals you need.

matches comes back one entry PER PERSON, in the order you sent them, with null where nothing matched. An entry carrying _stub: true is an empty record Apollo minted and CHARGED for — count it as a failure, not as data.

Each match drops employment_history, the Apollo CRM account record and the employer's heaviest blocks by default — a lot of 10 overflowed MCP output limits. projection names them; full=True returns everything.

⚠️ Phone numbers are not in this response: with reveal_phone_number Apollo delivers them to oto minutes later and hands back a request_id — pass it to apollo_reveal_phone_result. Either reveal needs your own Apollo key; a plain match works on the shared one.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "people": "<people>"
    },
    "name": "apollo_bulk_match"
  }
}

Input Schema

NameRequiredDescriptionDefault
_orgNoOrg (id) de CET APPEL. `oto_list_orgs()`.
fullNoreturn every match untouched (employment history, CRM account, employer tech stack). Same price — this is about size.
_groupNoÉquipe (id) de CET APPEL, sous son org parente.
peopleYes1-10 entries. Each takes the same identifiers as apollo_match_person — `id` (surest, from apollo_search_people), `email`, `linkedin_url`, or a FULL name (`first_name` + `last_name`) with `domain`/`organization_name`. A weak entry is refused by INDEX before the whole lot is billed.
_run_idNoOBLIGATOIRE à chaque appel dès `run_start` ou `data_claim_next` : sans lui, l'écriture sur une ligne réservée est refusée. Hérité seulement si le serveur tient un run actif — n'y compte pas.
_projectNoProjet (id) de CET APPEL — jeton PRIMAIRE. `oto_project(op='list')`.
_instanceNoInstance de connecteur PRÉCISE (un credential exact). `oto_instance(op='list')`.
reveal_phone_numberNoorder phone numbers (your own key); they are collected with apollo_reveal_phone_result, not returned here.
reveal_personal_emailsNoalso return PERSONAL emails (your own key).

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations at all, the description carries the full burden and does so well: it discloses per-person billing, the rate-limit-only benefit, the ordering and null-fill of `matches`, that `_stub: true` entries are charged failures, which fields are dropped by default, and that phone reveals are asynchronous and need the caller's own Apollo key while a plain match works on the shared key. These are exactly the non-obvious behaviors an agent must know before invoking a paid operation.

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 the core claim and the sibling contrast, then layered into cost, response shape, field pruning, and the phone follow-up. Dense and mostly every sentence earns its place, though the caps, emoji warnings, and rhetorical phrasing ('the way a list actually gets built') add theatrical weight beyond strict necessity.

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?

For a 9-parameter, paid, multi-entry batch tool with no output schema and no annotations, the description covers pricing semantics, return structure, stub handling, default field pruning, and the async phone flow — everything an agent needs to call it correctly and interpret the result.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: it explains the tradeoff behind `full=True` (same price, only size differs), clarifies that reveal parameters require a personal key and return a `request_id` rather than data, and describes the identifier combinations accepted per entry. Minor flaw: it references a `projection` parameter for naming dropped fields, which does not exist in the input schema.

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?

The description states a specific verb and resource with scope ('Match UP TO 10 people in one call') and immediately positions it against two named siblings: apollo_search_people (which produces the obfuscated records) and apollo_match_person (the one-at-a-time alternative). An agent can select this tool without opening any schema.

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

Usage Guidelines5/5

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

It gives an explicit when-to-use chain ('apollo_search_people returns hundreds... This is how you resolve them'), an explicit cost/workload comparison against apollo_match_person (300 people = 30 calls, not 300), and an explicit when-not caveat that batching does not save credits. It also names the follow-up tool for phones (apollo_reveal_phone_result).

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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