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Funkel AI

find_leads

Read-onlyIdempotent

Search leads in the app the request is scoped to. Returns bounded named summaries and IDs, never full rows. Use get_lead to load full detail for a specific id. Default source is 'discovered' (agent-found, awaiting approval); pass source='approved' to search leads already in campaigns. Optional 'search' keyword runs an ILIKE across name/company/headline; optional 'status' narrows by lead status. If results are thin, surface the count and ASK the user before broadening the filters — do not silently relax constraints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitYesMax number of leads to return (1..50)
searchNoOptional keyword (matches name, company, or headline)
sourceNoLead source. Default 'discovered'.
statusNoOptional status filter (e.g. 'pending', 'approved', 'rejected')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint/idempotent/destructive, so the safety profile is covered; the description goes beyond them by disclosing the return boundary ('bounded named summaries and IDs, never full rows') and the semantic meaning of the default source (agent-found, awaiting approval) plus the escalation policy on thin results. That is real behavioral context, not a restatement of 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?

Dense but front-loaded: purpose and scope first, then the alternative tool, then the source default, then filter behavior, then the escalation rule. No filler sentences and no repetition of schema constraints such as the 1..50 limit.

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?

There is no output schema, so the description carries the burden of describing returns — which it does ('bounded named summaries and IDs, never full rows'). Combined with the default-source semantics and the explicit alternative tool, an agent has everything needed to call this correctly.

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 baseline is 3, but the description adds genuine meaning: what 'source' values signify (discovered = agent-found awaiting approval vs approved = already in campaigns) and that 'search' runs an ILIKE across name/company/headline. 'status' only gets a restatement, keeping this just short of a 5.

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+resource ('Search leads') along with its scope ('the app the request is scoped to'), which distinguishes it from search_external_leads and other list/pull tools in the sibling set. It also names get_lead as the detail-loading counterpart, so an agent can route correctly without opening either schema.

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?

Explicit routing: 'Use get_lead to load full detail for a specific id.' Explicit default and override for source ('discovered' by default; pass source='approved' for leads already in campaigns). It even prescribes behavior when results are thin ('surface the count and ASK the user before broadening the filters — do not silently relax constraints'), which is guidance an agent could not infer.

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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