Spaces by Peakflo

graph_entities

Read-only

Discover entities in Business Context -> Graph. Search by type, source system, or name. Returns entities with their relationships. Also returns entity type schema with common questions — use graph_ask_question with a question ID to execute a predefined query.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "graph_entities"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Max results (default 20)
`entity_type`	No	Filter by entity type (e.g., vendor, customer, bill, invoice, decision)
`name_search`	No	Fuzzy name search
`source_system`	No	Filter by source system (e.g., golden, peakflo, netsuite, xero)
`include_linked_datastores`	No	Include linked datastores for each entity (default false). Adds extra DB queries — only set true when you need datastore context.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 5, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds valuable return context (entities with relationships, plus entity type schema with common questions), but says nothing about result ordering, pagination limits, or how expensive broad searches are.

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?

Three short sentences with the core purpose front-loaded and the graph_ask_question handoff stated last where it reads naturally. No filler, though the 'Business Context -> Graph' phrasing is slightly opaque.

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

Completeness4/5

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

With no output schema, the description correctly spends words on the return shape (entities plus relationships, plus entity type schema with questions). Combined with fully documented parameters and read-only annotations, an agent has enough to invoke it correctly; only result-set sizing and ordering behavior are unstated.

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 description coverage is 100%, so baseline is 3, and the description matches the schema by naming type, source system, and name as the searchable dimensions. It adds little beyond that — no syntax or matching-behavior detail — so it sits just above baseline rather than clearly enriching 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 uses a specific verb and resource ('Discover entities in Business Context -> Graph') and enumerates the searchable facets (type, source system, name). It also distinguishes itself from graph_ask_question by explaining that the question IDs it returns are consumed there, though it doesn't differentiate itself from other graph siblings such as graph_resolve or graph_precedent.

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?

Usage is implied: discover entities first, then use graph_ask_question with a returned question ID for predefined queries. That is a useful forward-routing hint, but there is no explicit statement of when to prefer this over graph_resolve, graph_precedent, or graph_linked_datastores, and no exclusions.

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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