noticed

search_people

Read-onlyIdempotent

Search people by free text — name, company, keywords. Terms are OR-matched and ranked by how many match (more terms broadens, not narrows). To narrow: put AND between terms to require all (e.g. 'health AND medtech'), or prefix a term with + to require just it (e.g. '+rust berlin'). For roles/functions (founder, engineer, investor, …) use the role filter instead of free text — it catches title variants ('Founding Partner') that keywords miss, and free text over-matches bios/notes. To count people of a type, use structured filters and read total from the response — a free-text total counts keyword matches, not people of that type. scope:'own' (default) / scope:'public' (beyond your network + warm-intro paths). Optional company, location, skills, tags filters. A structured-filter zero = thin data, not absence — fall back to free text. Misspelled names/companies fall back to fuzzy matching (fuzzy: true = closest matches — confirm before trusting); concept queries with zero literal hits fall back to embedding similarity (semantic: true = related people, not literal matches). Free-text responses also return strong_total (rows matching ALL terms — the honest count) and per-row matched_on (which fields matched). In scope:'public', role/location/company/skills are applied to the global hits (filtered: true); filters that cannot apply there are listed in unsupported_filters.

Input Schema

TableJSON Schema

Name	Required	Description
`q`	No	Free-text query — matched OR-style against name, headline, company, location, skills. Operators narrow your own network: uppercase AND between terms requires all of them; a +term prefix requires just that term (e.g. '+health medtech'). In scope:'public', operators only affect the your-network half of results; global hits are matched by semantic similarity.
`role`	No	Filter by function (matched against headline): engineer, designer, product, gtm, founder, recruiter, investor, other. `gtm` covers sales/growth/marketing/partnerships/biz-dev; `other` is the catch-all for customer success, operations, legal, academia, etc. Matches any of the listed roles. Seniority (CEO, CTO, VP, etc.) is not a function — pass it in `q` instead.
`tags`	No	Require all of these tags you've applied.
`limit`	No	Page size (default 25, max 200 for own / 50 for public).
`scope`	No	'own' (default) — your network only. 'public' — global match + warm-intro paths to people you don't know yet. 'team' — your POOLED TEAM network (people known by ANY teammate); each hit carries `team_members` (who on the team knows them + their consent-gated strength).
`stale`	No	Only people you've never interacted with, or not in 90+ days.
`offset`	No	Pagination offset.
`recent`	No	Only people you've interacted with in the last 14 days.
`skills`	No	Require all of these skills.
`company`	No	Filter by company (matches current + former employers).
`sources`	No	Restrict to people who came from any of these sources.
`team_id`	No	For scope:'team' — the organizationId of the team to search (from account_status.teams). Optional when you're in exactly one team; required to pick when you're in more than one. A team you don't belong to is rejected.
`location`	No	Filter by location (substring match).
`added_after`	No	Only people added to your network ON/AFTER this date (ISO, e.g. 2026-06-01). For "who did I add this week / since X?". Adding ≠ interacting — for interaction recency use recent/stale.
`added_before`	No	Only people added STRICTLY BEFORE this date (ISO).
`last_interaction_after`	No	Only people whose last recorded touchpoint was ON/AFTER this date (ISO, e.g. 2026-06-22 or 2026-06-22T00:00:00Z). Use for 'who did I interact with on/since <date>' or 'everyone with a touchpoint this week'. People with no interaction are excluded. Combine with last_interaction_before for a date range.
`last_interaction_before`	No	Only people whose last recorded touchpoint was STRICTLY BEFORE this date (ISO, exclusive upper bound). People with no interaction are excluded. Combine with last_interaction_after for a date range.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	Whether noticed completed the operation.
`data`	No	The operation result when ok is true.
`error`	No	A human-readable error when ok is false.

Schema Changelog

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

Changed1 schema field changedAug 22, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "data": {
+      "additionalProperties": true,
+      "description": "The operation result when ok is true.",
+      "properties": {
+        "hasMore": {
+          "type": "boolean"
+        },
+        "hits": {
+          "items": {
+            "$ref": "#/properties/data/properties/people/items"
+          },
+          "type": "array"
+        },
+        "limit": {
+          "type": "number"
+        },
+        "offset": {
+          "type": "number"
+        },
+        "people": {
+          "items": {
+            "additionalProperties": true,
+            "properties": {
+              "company": {
+                "$ref": "#/properties/data/properties/people/items/properties/person_id"
+              },
+              "display_name": {
+                "type": "string"
+              },
+              "headline": {
+                "$ref": "#/properties/data/properties/people/items/properties/person_id"
+              },
+              "id": {
+                "type": "string"
+              },
+              "name": {
+                "type": "string"
+              },
+              "person_id": {
+                "type": [
+                  "string",
+                  "null"
+                ]
+              }
+            },
+            "type": "object"
+          },
+          "type": "array"
+        },
+        "scope": {
+          "enum": [
+            "own",
+            "team",
+            "public"
+          ],
+          "type": "string"
+        },
+        "strong_total": {
+          "type": "number"
+        },
+        "total": {
+          "type": "number"
+        }
+      },
+      "type": "object"
+    },
+    "error": {
+      "description": "A human-readable error when ok is false.",
+      "type": "string"
+    },
+    "ok": {
+      "description": "Whether noticed completed the operation.",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "ok"
+  ],
+  "type": "object"
+}

First observedAug 22, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already mark this as read-only and idempotent, but the description goes far beyond them: OR-matching and ranking, AND and + operators, fuzzy matching fallback, semantic embedding fallback, strong_total semantics, matched_on per-row output, and unsupported_filters for public scope. This is rich behavioral disclosure that meaningfully informs invocation and result interpretation.

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?

The description is dense and long, but nearly every sentence earns its place given the parameter count and matching complexity. It is front-loaded with the core purpose and operator rules, though the wall-of-text format with many parenthetical asides could be more scannable.

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 17 parameters, an output schema, and safety annotations, this description still adds the missing decision context: fuzzy vs semantic fallback, what strong_total means, how filters behave in public scope, team_id requirements, and the meaning of zero structured-filter results. The tool is complex, and the description is complete enough for correct selection and invocation.

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

Parameters5/5

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 substantially enriches parameter meaning: q gets operator semantics, role gets coverage details and seniority guidance, scope gets whole-network vs warm-intro vs pooled-team distinctions, and date filters get interaction-vs-addition semantics. This is far beyond what the schema alone provides.

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 opens with 'Search people by free text — name, company, keywords', naming a specific verb and resource. It clearly distinguishes itself from sibling tools like get_person and list_identity_matches by framing this as a search operation with rich matching semantics.

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?

Provides explicit when-to-use guidance: use the role filter instead of free text for roles/functions, use structured filters to count people of a type, and fall back to free text when a structured-filter zero means thin data rather than absence. It also explains scope differences and operator behavior, giving an agent clear decision rules.

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

B3.4/5.0

Disambiguation3/5

The tool set is organized around distinct resources, and the descriptions work hard to separate them, but several close pairs remain easy to confuse: add_memory vs memory_save vs add_note, accept_identity_match vs suggest_identity_match, and dismiss_identity_match vs mark_different_people. An agent will often need to read very subtle signals (who originated the content, pending vs initiating a merge, soft vs durable rejection) to pick the right tool.

Naming Consistency3/5

Most tools follow a clear verb_noun snake_case pattern like create_list, update_person, and delete_view, which is readable and mostly predictable. However, the memory tools break the pattern (memory_save, memory_get, memory_search instead of save_memory/get_memory/search_memory), and a few noun-style names (my_profile, network_summary, account_status) add inconsistency.

Tool Count1/5

At 57 tools, this is an extremely large surface that exceeds the calibration threshold for an extreme mismatch. The scope is broad, but many tools are micro-specialized variations of the same concept, such as four memory-related tools and seven identity-match tools, which makes the count feel inflated rather than well-scoped.

Completeness4/5

The tool set provides thorough lifecycle coverage for the core domain: people can be added, updated, searched, and removed; lists, views, actions, and scheduled tasks have create/read/update/delete; and identity matching has accept, dismiss, differentiate, and suggest paths. Minor gaps exist, such as no direct memory/note deletion or intro deletion, but agents can generally complete workflows without hitting dead ends.

Resources

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