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Diffbot

by io.github.pipeworx-io

Diffbot Company

diffbot_company
Read-onlyIdempotent

Look up company details (employees, revenue, industry) for — enrich a company from the Diffbot Knowledge Graph. Returns description, homepage, employee count, revenue, industries, founding date, HQ location, CEO, and Twitter. Example: diffbot_company({ name: "Stripe", _apiKey: "your-token" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoOptional company homepage URL to disambiguate the match, e.g. "https://stripe.com"
nameNoCompany name to enrich, e.g. "Stripe". Required unless `url` is given.
_apiKeyYesDiffbot API token (free 10,000-credit tier, no card, at diffbot.com)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-diffbot-api-key",
+    "name": "Stripe"
+  },
+  {
+    "_apiKey": "your-diffbot-api-key",
+    "name": "OpenAI",
+    "url": "https://openai.com"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, indicating safe, non-destructive, idempotent reads. The description adds behavioral context by listing the exact return fields (e.g., description, homepage, employee count), providing transparency beyond the schema. No contradictions.

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?

The description is two concise sentences plus a code example, front-loading the main purpose and return values. Every sentence adds value, and the example demonstrates usage. No unnecessary words or repetition.

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?

Despite no output schema, the description lists all expected return fields (description, homepage, employee count, revenue, industries, etc.), which is critical for an agent. Parameters are fully documented via schema, and the purpose is clear. The tool is well-specified for its complexity.

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 100% with descriptions for all three parameters. The description adds little beyond the schema: it reiterates the use of 'name' and '_apiKey', and mentions the optional URL for disambiguation, but the schema already covers these details. Baseline 3 is appropriate.

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 clearly states the tool's purpose: looking up company details from the Diffbot Knowledge Graph. It lists specific return fields (employees, revenue, industry, etc.) and provides an example, making it easy to understand. Among siblings, it is distinct from general extraction tools like 'diffbot_extract' or 'entity_profile'.

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 description implies usage for enriching a company by name or URL, with an example. It does not explicitly state when to use versus siblings or provide exclusions, but the purpose is clear enough for an agent to decide. Lacking explicit when-not or alternative tool references.

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

A3.6/5.0
Disambiguation2/5

Multiple research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and company analysis tools (entity_profile, compare_entities, recent_changes, bet_research) overlap heavily; the five polymarket_* tools also require careful reading to distinguish. Descriptions are detailed, but an agent must parse long disambiguation text to avoid mis-selection.

Naming Consistency2/5

Names mix verb-first (list_subscriptions, validate_claim, remember), noun-first (entity_profile, polymarket_arbitrage), product-prefixed (ask_pipeworx, pipeworx_feedback), and brand-prefixed (diffbot_company, diffbot_extract). No consistent verb_noun pattern across the set; polymarket_* and pipeworx_* prefixes are internally consistent but the overall scheme is chaotic.

Tool Count2/5

33 tools is well beyond the 3-15 sweet spot and in the 'too many' range. Many tools are meta-variants (4 ask_pipeworx flavors, 5 polymarket tools, 2 AI-visibility tools) that could be consolidated.

Completeness3/5

The data-research, prediction-market, memory, and subscription subdomains are each fairly complete at a meta level, with few dead ends. Gaps include no subscription update (delete + recreate required), no explicit memory update, and no direct way to execute an arbitrary discovered Pipeworx tool aside from routing through ask_pipeworx.

Resources