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Glama

signals

by com.datahyena

investors

Read-only

Search and filter investor records by keyword, type, HQ country, or exact domain. Costs one credit per returned record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoInvestor type filters. Canonical values: vc, angel, cvc, pe, growth, accelerator, family_office, syndicate, unknown.
limitNoMaximum records to return (1-100). Each returned record costs one credit.
sinceNoReturn investors created/updated on or after this date (YYYY-MM-DD) or ISO-8601 timestamp.
cursorNoOpaque pagination cursor returned as pagination.nextCursor on the previous page.
domainNoExact investor domain.
searchNoKeyword search over investor name and domain.
countriesNoHeadquarters country codes (ISO alpha-2, OR). Pass "unknown" to include companies with no country on file.
investsInNoOnly investors that have funded companies headquartered in these countries (OR), e.g. ["US"]. Filters on where an investor deploys rather than where it is registered, and is far better covered than HQ country.
minRoundsNoOnly investors that have participated in at least this many rounds.
activeSinceNoOnly investors whose most recent round was announced on or after this date (YYYY-MM-DD).
minRoundUsdNoOnly investors that have participated in at least one round at or above this amount, in whole USD. Filters on behaviour rather than on a type label, which is sparse.
hasFirmographicsNoOnly investors whose domain and type we have resolved.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already mark this as read-only and non-destructive; the description reinforces that with 'Search and filter' and adds a useful economic constraint: 'Costs one credit per returned record.' This credit cost is material behavioral information beyond what annotations provide, though pagination and result format are not disclosed.

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?

Two short sentences with the core action and filters first, followed by the cost warning. No filler, no redundancy with the schema, and every sentence earns its place.

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?

The description gives the essential purpose and cost, and the schema covers all inputs, but with 12 optional filters, no output schema, and no pagination or return-format note, an agent may not know the full breadth of filters (e.g., investsIn, minRounds, activeSince) or what a returned record contains. Adequate but with clear gaps.

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?

All 12 parameters are fully described in the schema (100% coverage), so the description is not required to repeat them. It echoes the main filters (type, countries, domain, search) and adds the credit-cost nuance on limit, but adds no new semantics beyond the schema. Baseline 3 applies.

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 uses the action verbs 'Search and filter' on 'investor records' and names the key dimensions (keyword, type, HQ country, exact domain), making it clear what resource this tool operates on. This distinguishes it from resource-specific siblings like companies, funding_events, or acquisitions.

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 implies usage by naming the resource and filter dimensions, but it does not explicitly state when to prefer this tool over alternatives or when not to use it. There are no exclusions or references to sibling tools, so the agent must infer that investor-record queries go here.

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

A4/5.0
Disambiguation5/5

Each tool targets a clearly distinct resource or action: event types (acquisitions, funding_events, exec_moves), entities (companies, investors), and watchlist operations. The watchlist CRUD commands are separated by function, and company_timeline is clearly scoped to a single company's full history.

Naming Consistency4/5

Watchlist tools consistently use verb_noun (create_watchlist, get_watchlist, add_to_watchlist), while data lookup tools use plural nouns describing the returned entity or event (companies, investors, funding_events). The mixed convention is readable and predictable, with only company_timeline deviating from both patterns.

Tool Count5/5

With 11 tools, the server is well-scoped for its domain: company/event data search plus watchlist management. Each tool has a defined role and the count feels neither sparse nor bloated.

Completeness4/5

The toolset covers the core domain well: company and investor search, major event types, company timelines, and full watchlist membership management. A notable minor gap is the absence of a delete_watchlist tool, since create/list/get/add/remove exist for watchlists.

Resources