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Glama

Permid

by io.github.pipeworx-io

Get Entity

get_entity
Read-onlyIdempotent

Fetch the raw JSON-LD PermID record for a numeric PermID (e.g. "4295905573"). Returns the full linked-data payload from api-eit.refinitiv.com under the key "raw" — extract organization name, LEI, RIC, industry codes, and headquarters from the raw fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
permidYesPermID (numeric string, e.g., "4295905573")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawYesFull JSON-LD record from PermID API
permidYesPermID numeric identifier
permid_urlYesFull PermID URL for the entity

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "permid": "4295905573"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "permid": {
+      "description": "PermID numeric identifier",
+      "type": "string"
+    },
+    "permid_url": {
+      "description": "Full PermID URL for the entity",
+      "type": "string"
+    },
+    "raw": {
+      "description": "Full JSON-LD record from PermID API",
+      "type": "object"
+    }
+  },
+  "required": [
+    "permid_url",
+    "permid",
+    "raw"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already provide safety traits (readOnly, idempotent, non-destructive). The description adds significant behavioral context: it discloses the source API, the return structure (key 'raw'), and the extractable fields (name, LEI, RIC, etc.), enhancing understanding beyond the annotations.

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 with no wasted words. The first sentence states the core action with a concrete example, and the second explains the output and potential use cases. Information is front-loaded and easy to parse.

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?

Given the tool's simplicity (one parameter), rich annotations, and presence of an output schema, the description provides complete context. It explains the tool's purpose, example input, and what to expect from the output, leaving no major 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?

Schema coverage is 100%, and the schema already describes the parameter type and example. The description repeats the example but adds no new meaning or constraints beyond what the schema provides, so a score of 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 action ('Fetch') and the resource ('raw JSON-LD PermID record for a numeric PermID'). It specifies the exact return structure and gives an example PermID, making the tool's purpose unambiguous and differentiated from siblings like resolve_entity or search_entities.

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 retrieving raw linked-data records but does not explicitly guide when to use this tool versus siblings (e.g., resolve_entity, entity_profile). However, the clear purpose and mention of 'raw JSON-LD' effectively imply its niche.

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
Disambiguation4/5

Most tools have clearly distinct purposes, but the set includes three very similar `ask_pipeworx` variants that could cause confusion despite detailed descriptions. The Polymarket and memory tools are well-differentiated.

Naming Consistency3/5

Names follow a mix of patterns: some `verb_noun` (ask_pipeworx, compare_entities), some `noun_noun` (entity_profile, polymarket_arbitrage), and some standalone nouns (forget, recall). While readable, the inconsistency is notable.

Tool Count3/5

At 33 tools, the server is on the heavy side for typical MCP servers (3-15 ideal). However, the broad scope covering many data domains partially justifies the count.

Completeness4/5

The tool set covers a wide range of data access and analysis needs, including entity resolution, company profiles, betting analysis, subscriptions, and memory. Minor gaps exist (e.g., no direct tool for editing entities), but overall it's well-rounded.

Resources