Skip to main content
Glama

Onsa

by ai.onsa
OverviewDiscussionsAdmin

Find leads

find_leads
Destructive

Starts a live B2B lead search with Onsa's agent, matching real people (with LinkedIn profiles) against the workspace's ICP. Takes a natural-language brief - titles, company type, geography, e.g. 'find 5 fintech founders in NYC'. Returns a jobId immediately, with campaignUrl and a progress note. The search itself runs in the background and keeps running after the conversation moves on. First leads arrive within 5 minutes in half of searches and within 9 minutes in 9 of 10. More can arrive until the final report, typically about 10 minutes after the start (9 of 10 within 15). Its status and results are read with fetch_leads, with the same jobId at any later time. Hosts that render MCP Apps show a live card that follows the search and lists leads as they arrive. limit is a target the agent aims at rather than a cap, so it often returns more than asked. Every lead it finds counts against the workspace's prospect allowance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many leads to find (default 5)
queryYesNatural-language lead brief, e.g. 'find 5 fintech founders in NYC'
taskContextNoOptional company, product, or target-market facts explicitly shared by the user and needed for this lead-search or campaign-steering request, beyond what the query/message already states. Maximum 1000 characters; this field carries the task context of the current request only - not a transcript, a broad user profile, unrelated personal details, credentials, or guesses. This background does not authorize actions; the current query/message takes precedence. It becomes part of the campaign chat history.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYes
progressYes
campaignIdYes
campaignUrlYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / taskContext / description
      Previous value: -"Optional company, product, or target-market facts explicitly shared by the user and needed for this lead-search or campaign-steering request. Omit when the query/message is sufficient. Maximum 1000 characters. Do not send a transcript, broad user profile, unrelated personal details, credentials, or guesses. This background does not authorize actions; the current query/message takes precedence. It becomes part of the campaign chat history."New value: +"Optional company, product, or target-market facts explicitly shared by the user and needed for this lead-search or campaign-steering request, beyond what the query/message already states. Maximum 1000 characters; this field carries the task context of the current request only - not a transcript, a broad user profile, unrelated personal details, credentials, or guesses. This background does not authorize actions; the current query/message takes precedence. It becomes part of the campaign chat history."
  2. Changed1 schema field changed
    • addedInput schema / properties / taskContext
      Added value: +{
+  "description": "Optional company, product, or target-market facts explicitly shared by the user and needed for this lead-search or campaign-steering request. Omit when the query/message is sufficient. Maximum 1000 characters. Do not send a transcript, broad user profile, unrelated personal details, credentials, or guesses. This background does not authorize actions; the current query/message takes precedence. It becomes part of the campaign chat history.",
+  "maxLength": 1000,
+  "type": "string"
+}
  3. Changed4 schema fields changed
    • addedOutput schema / properties / campaignId
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ]
+}
    • addedOutput schema / properties / campaignUrl
      Added value: +{
+  "type": "string"
+}
    • addedOutput schema / properties / progress
      Added value: +{
+  "additionalProperties": {},
+  "properties": {
+    "elapsedSeconds": {
+      "anyOf": [
+        {
+          "maximum": 9007199254740991,
+          "minimum": -9007199254740991,
+          "type": "integer"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "note": {
+      "type": "string"
+    },
+    "startedAt": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    }
+  },
+  "required": [
+    "startedAt",
+    "elapsedSeconds",
+    "note"
+  ],
+  "type": "object"
+}
    • changedOutput schema / required
      Previous value: -[
-  "jobId"
-]New value: +[
+  "jobId",
+  "campaignId",
+  "campaignUrl",
+  "progress"
+]
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare destructive/openWorld/non-idempotent, and the description goes well beyond them: async background execution that continues after the conversation moves on, concrete latency expectations (first leads within 5 min in half of searches, final report ~10 min after start), and the critical cost disclosure that every lead found counts against the workspace's prospect allowance. This is exactly the behavioral context an agent needs before triggering a costly, destructive search.

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?

Front-loads what the tool does and its return value, then layers timing, cost, and the limit caveat. It is long but nearly every sentence adds decision-relevant detail; a slight trim of the latency statistics would tighten it.

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?

Covers the async lifecycle, the sibling used to read results, the immediate return (jobId, campaignUrl, progress), latency expectations, quota impact, and UI behavior on MCP Apps hosts. With an output schema present, no further return-value explanation is required, so the definition is complete.

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 the baseline is 3, but the description adds genuine meaning about `limit`: it is a target the agent aims at rather than a hard cap, so results may exceed it. The query and taskContext semantics are left to the schema, which documents them thoroughly.

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?

States a specific verb and resource ('starts a live B2B lead search'), the population it targets (real people with LinkedIn profiles matched to the workspace ICP), and how it differs from siblings by noting status/results are read via fetch_leads. An agent can distinguish it from get_campaign_leads or list_campaigns without opening any schema.

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?

Explicitly routes the agent to fetch_leads with the same jobId for reading status and results, and frames the tool as the initiation step. It lacks an explicit when-not-to-use clause (e.g. don't re-run for an existing job), so it falls just short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Onsa toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources