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by com.getsliq
OverviewDiscussionsAdmin

List people

list_people
Read-only

Each row: id (the person id query_people uses), display_name, spine (title, company, location, headline), current_roles (every role the person holds now in LinkedIn's order, each {position, company, start}; the spine's title and company are only the first of them, and an empty list means no role in their history is dated as ongoing, not that they have no job), company_profile_id (their company's list_companies id, or null), linkedin_slug, linkedin_provider_id, email, outreach_stage and removed. Removing someone from a campaign leaves their outreach_stage as it was. With include_signals it also has signal_entries, the signals the person's profile shows, newest first and the same with or without agent_id: their company's, and the one each of their campaigns enrolled them on. Each entry's company_id and company_name are the company the signal belongs to, which can differ from the person's own; they are null for an enrollment signal no company row holds. They cost a few database reads per person, so ask for them on a narrow read (a q, one bucket, or a small limit).

Without agent_id, each row also has:

  • outreach_stage: the person's most advanced lead-funnel bucket across the campaigns of the user and every teammate, '' when they're in none.

  • removed: true when they were removed from every one of those campaigns they're in.

  • activity: those campaigns' prospect rows on the person, newest first, each {owner_email, owner_display_name, agent ({id, title}, or null), stage, removed, last_touch_at}, where removed is whether they were removed from that campaign.

With agent_id, each row also has:

  • degree: the user's LinkedIn degree to the person, '1', '2', '3' or 'out_of_network'; null when unknown. A person the user is connected to reads '1'.

  • intro: the warm-intro check. state is one of not_checked, checking, queued, found, cold, connected, unresolved. connectors lists the user's 1st-degree connections who know the person (name, headline, identifier, provider_id). mutual_connections_truncated true means connectors is a sample of a longer list. shared_connections_count is LinkedIn's total. result_id is the search row the check read, null when the person has none.

  • outreach_stage: the person's lead-funnel bucket on this agent, '' when not in outreach here. prospect_id, email_stage and linkedin_stage are their prospect row on this agent, null when not enrolled. removed is true when they were removed from the agent's campaign, and removal_reason says why, '' when they weren't or no reason was recorded.

  • lists and sources: the search lists and discovery tools that surfaced the person. columns holds the researched [label, value] pairs.

  • verdict, verdict_reason and rejected_on: the curation recorded on the person's search rows, as the Verdict column shows it. 'rejected' when every row is rejected, 'qualified' when any row is qualified, else 'unclear' (screened but too thin to judge) when any row is, else null, with the reason from the latest row carrying it. On a group_by='list' bucket page, or under a "run" filter whose runs all saved to one list, it's the verdict on that list's row. rejected_on is {lists, reason}: the lists whose own verdict is 'rejected' while the person's verdict isn't, with the latest rejected row's reason; its lists is empty otherwise and on those two reads. A person rejected on every row is left out unless a "verdict" filter asks for 'rejected', a "run" filter is set or include_rejected is set — the same as the user's default view — though a tracked prospect always shows.

  • criteria, only with include_criteria: why the person matched, as their opened row shows it. Per list they're in, {list_name, evaluations}; each evaluation is a criterion with the reasoning, a verdict (satisfied 'yes', 'no' or 'unclear', or a numeric score where 7 and up is met) and http(s) references. Criteria run about 2KB per person, so ask for them on a narrow read (a q, one bucket, or a small limit) or from run_code. A list where all of the person's rows are rejected is left out unless the read includes rejected people.

The "list", "source", "degree" and "connector" groupings, the "list", "source", "degree", "verdict" and "run" filters, include_criteria and include_rejected read one agent, so they need agent_id.

To count people per group, pass group_by alone. To list one group's people, pass group_by plus a bucket key taken from those counts. Otherwise you get everyone, sorted by name. Page until next_cursor is null by passing it back as cursor. A direct call returns 25 rows by default; for a wide pull, call this from run_code with limit up to 200 (nothing truncates there) and print only the fields you need. A dict. A group_by without a bucket returns group_counts, a list of {key, count} with the largest group first and the '' group last ("degree" keeps closest-first order and "segment" its group's tag order then 'No match'; "connector" entries add label), and total, the matching people each counted once. With group_by="list", each group also carries criteria: the search criteria a Websets list screens its people against, '' for any other list. Every other call returns results (rows shaped as above) and next_cursor, plus total (every matching person) when group_by is omitted. With agent_id, every call also returns filtered_out_count: how many people this same call matches once a "verdict" filter on 'rejected' is added to filters, with the same q, group_by and segment_group_id and no bucket, each person counted once. Only under the same search, filters and grouping as the user's People table does it equal the number that table shows as "didn't match".

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": "list_people"
  }
}

Input Schema

NameRequiredDescriptionDefault
qNoA substring matched against name, title, headline and company.
limitNoRows per page (default 25, max 200).
bucketNoOne key from `group_counts` for the same `group_by`; returns that group's people. Needs `group_by`.
cursorNoThe `next_cursor` from the previous page; omit for the first page.
filtersNoClauses that must all match. For "degree", "outreach_stage", "list" or "source", use {"column_key": <dimension>, "operator": "is_any_of", "values": [keys]} with keys as group_counts returns them ('' matches no value). For "reached", use the same shape with funnel rungs ('contacted', 'replied', 'interested', 'meeting_booked'): it matches everyone the rung counts in the lead funnel's `reached` (everyone who got at least that far), so list a rung's people with it; "outreach_stage" matches only the people at that one stage. For "removed", use the same shape with 'removed' (the people removed from the agent's campaign, or without `agent_id` from every campaign they're in) or 'not_removed' (the people in the agent's campaign who weren't removed, at any stage, finished and replied included; without `agent_id`, the people in at least one campaign they weren't removed from); people not in the agent's campaign (without `agent_id`, in none) match neither. For "segment", use the same shape with the segment keys above. For "verdict", use the same shape with any of 'rejected' (the people the user's default view filters out, labelled "Didn't match" in their table), 'qualified' and 'unclear'; it matches each person's `verdict`, or with group_by="list" their verdict on each list, and 'rejected' brings the rejected people in without `include_rejected`. For "run", use the same shape with run ids from `query_search_runs` as strings; it matches the people those search runs found, rejected ones included. When those runs all saved to one list and group_by isn't "list", a "verdict" filter reads that list alone, as on its group_by="list" bucket page. For "name", "title", "headline", "company" or "location", use {"column_key": <column>, "operator": "contains", "text": <substring>}.
agent_idNoThe agent whose People tab to read. Omit it to read everyone in the account.
group_byNoThe dimension to count or to pick a bucket from. "degree" keys are '1', '2', '3', 'out_of_network'; "connector" keys are a connector's provider_id (else their identifier or name) and carry the connector's name as `label`; "outreach_stage" keys are funnel buckets; "list" and "source" keys are list names and discovery tools; "segment" keys are tag names in `segment_group_id`'s group, plus 'No match' for people the classifier couldn't place; "title", "company" and "location" key on the person's own value. A '' key is the group with no value (for "segment", people the group hasn't classified). A person with several lists, sources, connectors or tags counts in each.
company_idNoOnly the people at this company (a company `id` from list_companies or query_companies).
on_behalf_ofNoA teammate's email, or an agency client's from list_agency_clients, to run this call in that account: their agents, settings, connected accounts, and credits. Omit it to act on your own account.
include_signalsNoAlso return each row's `signal_entries`. Default False.
include_criteriaNoAlso return each row's `criteria`. Default False.
include_rejectedNoAlso return (and count) the people left out as rejected. Default False.
segment_group_idNoThe segment group (from list_segment_groups or get_segment_funnel) a "segment" filter or group_by reads. With it, each row also carries `segment`, its tag names in that group.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / on_behalf_of / description
      Previous value: -"An agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."New value: +"A teammate's email, or an agency client's from list_agency_clients, to run this call in that account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."
  2. Changed2 schema fields changed
    • changedInput schema / properties / filters / anyOf
      Previous value: -[
-  {
-    "items": {
-      "oneOf": [
-        {
-          "properties": {
-            "column_key": {
-              "description": "A person's own text column.",
-              "enum": [
-                "name",
-                "title",
-                "headline",
-                "company",
-                "location"
-              ],
-              "title": "PersonTextKey",
-              "type": "string"
-            },
-            "operator": {
-              "const": "contains",
-              "type": "string"
-            },
-            "text": {
-              "minLength": 1,
-              "type": "string"
-            }
-          },
-          "required": [
-            "column_key",
-            "operator",
-            "text"
-          ],
-          "title": "PersonTextClause",
-          "type": "object"
-        },
-        {
-          "properties": {
-            "column_key": {
-              "description": "A person dimension matched against a set of keys.",
-              "enum": [
-                "outreach_stage",
-                "reached",
-                "segment",
-                "list",
-                "source",
-                "degree",
-                "verdict",
-                "run"
-              ],
-              "title": "PersonValueKey",
-              "type": "string"
-            },
-            "operator": {
-              "enum": [
-                "is",
-                "is_any_of"
-              ],
-              "type": "string"
-            },
-            "values": {
-              "items": {
-                "type": "string"
-              },
-              "minItems": 1,
-              "type": "array"
-            }
-          },
-          "required": [
-            "column_key",
-            "operator",
-            "values"
-          ],
-          "title": "PersonValuesClause",
-          "type": "object"
-        }
-      ]
-    },
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "items": {
+      "oneOf": [
+        {
+          "properties": {
+            "column_key": {
+              "description": "A person's own text column.",
+              "enum": [
+                "name",
+                "title",
+                "headline",
+                "company",
+                "location"
+              ],
+              "title": "PersonTextKey",
+              "type": "string"
+            },
+            "operator": {
+              "const": "contains",
+              "type": "string"
+            },
+            "text": {
+              "minLength": 1,
+              "type": "string"
+            }
+          },
+          "required": [
+            "column_key",
+            "operator",
+            "text"
+          ],
+          "title": "PersonTextClause",
+          "type": "object"
+        },
+        {
+          "properties": {
+            "column_key": {
+              "description": "A person dimension matched against a set of keys.",
+              "enum": [
+                "outreach_stage",
+                "reached",
+                "removed",
+                "segment",
+                "list",
+                "source",
+                "degree",
+                "verdict",
+                "run"
+              ],
+              "title": "PersonValueKey",
+              "type": "string"
+            },
+            "operator": {
+              "enum": [
+                "is",
+                "is_any_of"
+              ],
+              "type": "string"
+            },
+            "values": {
+              "items": {
+                "type": "string"
+              },
+              "minItems": 1,
+              "type": "array"
+            }
+          },
+          "required": [
+            "column_key",
+            "operator",
+            "values"
+          ],
+          "title": "PersonValuesClause",
+          "type": "object"
+        }
+      ]
+    },
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / filters / description
      Previous value: -"Clauses that must all match. For \"degree\", \"outreach_stage\", \"list\" or\n\"source\", use {\"column_key\": <dimension>, \"operator\": \"is_any_of\", \"values\": [keys]}\nwith keys as group_counts returns them ('' matches no value). For \"reached\", use the\nsame shape with funnel rungs ('contacted', 'replied', 'interested', 'meeting_booked'):\nit matches everyone the rung counts in the lead funnel's `reached` (everyone who got at\nleast that far), so list a rung's people with it; \"outreach_stage\" matches only the\npeople at that one stage. For \"segment\", use the same shape with the segment keys\nabove. For \"verdict\", use the same shape with any of 'rejected' (the people the user's\ndefault view filters out, labelled \"Didn't match\" in their table), 'qualified' and\n'unclear'; it matches each person's `verdict`, or with group_by=\"list\" their verdict on\neach list, and 'rejected' brings the rejected people in without `include_rejected`.\nFor \"run\", use the same shape with run ids from `query_search_runs` as strings; it\nmatches the people those search runs found, rejected ones included. When those runs\nall saved to one list and group_by isn't \"list\", a \"verdict\" filter reads that list\nalone, as on its group_by=\"list\" bucket page.\nFor \"name\", \"title\", \"headline\", \"company\" or \"location\", use\n{\"column_key\": <column>, \"operator\": \"contains\", \"text\": <substring>}."New value: +"Clauses that must all match. For \"degree\", \"outreach_stage\", \"list\" or\n\"source\", use {\"column_key\": <dimension>, \"operator\": \"is_any_of\", \"values\": [keys]}\nwith keys as group_counts returns them ('' matches no value). For \"reached\", use the\nsame shape with funnel rungs ('contacted', 'replied', 'interested', 'meeting_booked'):\nit matches everyone the rung counts in the lead funnel's `reached` (everyone who got at\nleast that far), so list a rung's people with it; \"outreach_stage\" matches only the\npeople at that one stage. For \"removed\", use the same shape with 'removed' (the people\nremoved from the agent's campaign, or without `agent_id` from every campaign they're\nin) or 'not_removed' (the people in the agent's campaign who weren't removed, at any\nstage, finished and replied included; without `agent_id`, the people in at least one\ncampaign they weren't removed from); people not in the agent's campaign (without\n`agent_id`, in none) match neither.\nFor \"segment\", use the same shape with the segment keys above.\nFor \"verdict\", use the same shape with any of 'rejected' (the\npeople the user's default view filters out, labelled \"Didn't match\" in their table),\n'qualified' and 'unclear'; it matches each person's `verdict`, or with group_by=\"list\"\ntheir verdict on each list, and 'rejected' brings the rejected people in without\n`include_rejected`.\nFor \"run\", use the same shape with run ids from `query_search_runs` as strings; it\nmatches the people those search runs found, rejected ones included. When those runs\nall saved to one list and group_by isn't \"list\", a \"verdict\" filter reads that list\nalone, as on its group_by=\"list\" bucket page.\nFor \"name\", \"title\", \"headline\", \"company\" or \"location\", use\n{\"column_key\": <column>, \"operator\": \"contains\", \"text\": <substring>}."
  3. Changed2 schema fields changed
    • changedInput schema / properties / filters / anyOf
      Previous value: -[
-  {
-    "items": {
-      "oneOf": [
-        {
-          "properties": {
-            "column_key": {
-              "description": "A person's own text column.",
-              "enum": [
-                "name",
-                "title",
-                "headline",
-                "company",
-                "location"
-              ],
-              "title": "PersonTextKey",
-              "type": "string"
-            },
-            "operator": {
-              "const": "contains",
-              "type": "string"
-            },
-            "text": {
-              "minLength": 1,
-              "type": "string"
-            }
-          },
-          "required": [
-            "column_key",
-            "operator",
-            "text"
-          ],
-          "title": "PersonTextClause",
-          "type": "object"
-        },
-        {
-          "properties": {
-            "column_key": {
-              "description": "A person dimension matched against a set of keys.",
-              "enum": [
-                "outreach_stage",
-                "segment",
-                "list",
-                "source",
-                "degree",
-                "verdict",
-                "run"
-              ],
-              "title": "PersonValueKey",
-              "type": "string"
-            },
-            "operator": {
-              "enum": [
-                "is",
-                "is_any_of"
-              ],
-              "type": "string"
-            },
-            "values": {
-              "items": {
-                "type": "string"
-              },
-              "minItems": 1,
-              "type": "array"
-            }
-          },
-          "required": [
-            "column_key",
-            "operator",
-            "values"
-          ],
-          "title": "PersonValuesClause",
-          "type": "object"
-        }
-      ]
-    },
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "items": {
+      "oneOf": [
+        {
+          "properties": {
+            "column_key": {
+              "description": "A person's own text column.",
+              "enum": [
+                "name",
+                "title",
+                "headline",
+                "company",
+                "location"
+              ],
+              "title": "PersonTextKey",
+              "type": "string"
+            },
+            "operator": {
+              "const": "contains",
+              "type": "string"
+            },
+            "text": {
+              "minLength": 1,
+              "type": "string"
+            }
+          },
+          "required": [
+            "column_key",
+            "operator",
+            "text"
+          ],
+          "title": "PersonTextClause",
+          "type": "object"
+        },
+        {
+          "properties": {
+            "column_key": {
+              "description": "A person dimension matched against a set of keys.",
+              "enum": [
+                "outreach_stage",
+                "reached",
+                "segment",
+                "list",
+                "source",
+                "degree",
+                "verdict",
+                "run"
+              ],
+              "title": "PersonValueKey",
+              "type": "string"
+            },
+            "operator": {
+              "enum": [
+                "is",
+                "is_any_of"
+              ],
+              "type": "string"
+            },
+            "values": {
+              "items": {
+                "type": "string"
+              },
+              "minItems": 1,
+              "type": "array"
+            }
+          },
+          "required": [
+            "column_key",
+            "operator",
+            "values"
+          ],
+          "title": "PersonValuesClause",
+          "type": "object"
+        }
+      ]
+    },
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / filters / description
      Previous value: -"Clauses that must all match. For \"degree\", \"outreach_stage\", \"list\" or\n\"source\", use {\"column_key\": <dimension>, \"operator\": \"is_any_of\", \"values\": [keys]}\nwith keys as group_counts returns them ('' matches no value). For \"segment\", use the\nsame shape with the segment keys above. For \"verdict\", use the same shape with any\nof 'rejected' (the people the user's default view filters out, labelled \"Didn't\nmatch\" in their table), 'qualified' and 'unclear'; it matches each person's\n`verdict`, or with group_by=\"list\" their verdict on each list, and 'rejected' brings\nthe rejected people in without\n`include_rejected`. For \"run\", use the same shape with run ids from\n`query_search_runs` as strings; it matches the people those search runs found,\nrejected ones included. When those runs all saved to one list and group_by isn't\n\"list\", a \"verdict\" filter reads that list alone, as on its group_by=\"list\" bucket page.\nFor \"name\", \"title\", \"headline\", \"company\" or \"location\", use\n{\"column_key\": <column>, \"operator\": \"contains\", \"text\": <substring>}."New value: +"Clauses that must all match. For \"degree\", \"outreach_stage\", \"list\" or\n\"source\", use {\"column_key\": <dimension>, \"operator\": \"is_any_of\", \"values\": [keys]}\nwith keys as group_counts returns them ('' matches no value). For \"reached\", use the\nsame shape with funnel rungs ('contacted', 'replied', 'interested', 'meeting_booked'):\nit matches everyone the rung counts in the lead funnel's `reached` (everyone who got at\nleast that far), so list a rung's people with it; \"outreach_stage\" matches only the\npeople at that one stage. For \"segment\", use the same shape with the segment keys\nabove. For \"verdict\", use the same shape with any of 'rejected' (the people the user's\ndefault view filters out, labelled \"Didn't match\" in their table), 'qualified' and\n'unclear'; it matches each person's `verdict`, or with group_by=\"list\" their verdict on\neach list, and 'rejected' brings the rejected people in without `include_rejected`.\nFor \"run\", use the same shape with run ids from `query_search_runs` as strings; it\nmatches the people those search runs found, rejected ones included. When those runs\nall saved to one list and group_by isn't \"list\", a \"verdict\" filter reads that list\nalone, as on its group_by=\"list\" bucket page.\nFor \"name\", \"title\", \"headline\", \"company\" or \"location\", use\n{\"column_key\": <column>, \"operator\": \"contains\", \"text\": <substring>}."
  4. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, it discloses default exclusion of rejected people unless a verdict filter/run filter/`include_rejected` is set, special treatment of tracked prospects, the per-person database cost of `include_signals` and `include_criteria`, default sorting by name, 25-row pagination with `next_cursor`, and the 200-row `run_code` path. That is rich behavioral context an agent cannot infer from annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is front-loaded with a useful summary and returns section, but it is very long for a description. There is genuine duplication: `outreach_stage` and `removed` are defined in the row list and then again in the with/without-`agent_id` blocks, and the 'cost a few database reads, ask on a narrow read' warning appears twice for signals and criteria.

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?

For a 13-parameter tool with no formal output schema, the description explains return shapes, `group_counts` versus `results`, `total`, `next_cursor`, and `filtered_out_count` semantics in addition to all edge cases around rejected people and agent-scoped reads. An agent has everything needed to select, paginate, and interpret the call.

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 coverage is 100%, so the baseline is 3, but the description adds cross-parameter meaning: which `group_by` dimensions and filters require `agent_id`, how `bucket` keys come from `group_counts`, and the cost trade-off for `include_signals`/`include_criteria`. The schema already carries detailed per-parameter text, so the added value is real but incremental rather than decisive.

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 a specific verb and resource ('Read a People table as the user sees it, one row per person') and immediately distinguishes the two modes by `agent_id` (agent's People tab vs. account-wide Prospects > People). It also names the sibling it is not: query_people, reserved for one person's full record or columns these tables lack.

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?

It states exactly when to use this tool ('questions about a set of people: how many are in each outreach stage, title, company, location or segment') and when to reach for the alternative ('For one person's full record, or a filter on a column these tables don't offer... use query_people'). It further explains `group_by` without `bucket` for counts versus with `bucket` for listing and warns that certain groupings and filters require `agent_id`.

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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