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Leads search

run_leads_search
Read-onlyIdempotent

Find leads by person, location, company, industry, tech stack, funding, traffic, hiring and HQ filters, optionally within one company. Returns name, job title, seniority, department, location, LinkedIn profile, skills and company details; paged up to 500,000 pages of 10–100 leads. 2 points per returned record.

Input Schema

NameRequiredDescriptionDefault
abmNoABM. e.g. 6Sense, Demandbase, Engagio
cmsNoCMS. e.g. Adobe CQ, Adobe Experience Manager, Contentful
crmNoCRM. e.g. Hubspot, Microsoft Dynamics, Pipedrive CRM
erpNoERP. e.g. acumatica, deltek erp, epicor eclipse
cityNoCity
pageNoPage (up to 500000) Default: 1.
stateNoState / Province. e.g. Alabama, Alaska, Alberta
domainNoDomain (include)
skillsNoSkills
countryNoCountry. e.g. Afghanistan, Aland Islands, Albania
has_cioNoHas CIO. Company has a Chief Information Officer
hq_cityNoHQ City
revenueNoRevenue. e.g. $100M to <$1B, $10M to <$50M, $1B+
has_cisoNoHas CISO. Company has a Chief Information Security Officer
hq_stateNoHQ State. e.g. California
analyticsNoAnalytics. e.g. Looker, Power BI, Qlik
continentNoContinent. e.g. Africa, Antarctica, Asia
ecommerceNoE-Commerce. e.g. BigCommerce, Oracle Commerce, Salesforce Commerce Cloud
languagesNoLanguages
last_nameNoName: last
page_sizeNoLeads per page (10–100) Default: 25.
first_nameNoName: first
hq_countryNoHQ Country. e.g. Afghanistan, Aland Islands, Albania
job_levelsNoSeniority Level. e.g. C-Level, VP, Director
developmentNoDevelopment. e.g. Couchbase, MongoDB, Neo4j
has_web_appNoHas Web App. Company has a web application
app_securityNoApp Security. e.g. Checkmarx, Semgrep, Snyk
company_nameNoCompany Name
funding_typeNoFunding Type. e.g. Angel Round, Convertible Note, Corporate Round
min_ad_spendNoAd Spend
sic_industryNoSIC Industry. Standard Industrial Classification — 4-digit codes used for business categorization. e.g. Accident/health insurance carrier, Accounting/auditing/bookkeeping, Adjustment/collection services
email_hostingNoEmail Hosting. e.g. Google, Microsoft
employees_maxNoEmployees: maximum
employees_minNoEmployees: minimum
exclude_titleNoJob Title (exclude)
include_titleNoJob Title (include)
job_functionsNoDepartment. e.g. Advertising & Marketing, Art, Culture and Creative Professionals, Construction
min_investorsNoMin # Investors
cloud_providerNoCloud Provider. e.g. AWS, Alibaba, Google Cloud
cloud_securityNoCloud Security. e.g. Datadog, Lacework, Sysdig
email_securityNoEmail Security. e.g. Avanan, Barracuda, Egress
exclude_domainNoDomain (exclude)
has_mobile_appNoHas Mobile App. Company has a mobile application
lead_investorsNoLead Investors. e.g. Sequoia Capital
naics_industryNoNAICS Industry. North American Industry Classification System — standard used by US, Canada, and Mexico. e.g. ATV, Abrasive Product Manufacturing, Adhesive Manufacturing
person_headlineNoHeadline (include). e.g. B2B, B2C, SaaS
company_headlineNoHeadline (include). e.g. B2B, B2C, SaaS
company_overviewNoCompany Overview (include). e.g. B2B, B2C, SaaS
min_last_fundingNoMin Last Funding
min_paid_trafficNoPaid
sales_automationNoSales Automation. e.g. Apollo, Outreach, Salesloft
linkedin_industryNoIndustry. e.g. Accessible Architecture and Design, Accommodation and Food Services, Accounting
min_it_open_rolesNoIT
min_total_fundingNoMin Total Funding
min_total_trafficNoTotal Traffic
company_name_fuzzyNoCompany Name: fuzzy match
min_linkedin_growthNoLI Growth %
min_organic_trafficNoOrganic
min_sales_headcountNoSales
company_linkedin_urlNoCompany LinkedIn URL. e.g. https://www.linkedin.com/company/stripe
marketing_automationNoMarketing Automation. e.g. Hubspot, Klaviyo, Mailchimp
min_sales_open_rolesNoSales
min_devops_open_rolesNoDevOps
exclude_person_headlineNoHeadline (exclude). e.g. B2B, B2C, SaaS
min_marketing_headcountNoMarketing
exclude_company_headlineNoHeadline (exclude). e.g. B2B, B2C, SaaS
exclude_company_overviewNoCompany Overview (exclude). e.g. B2B, B2C, SaaS
min_marketing_open_rolesNoMarketing
conversation_intelligenceNoConversation Intelligence. e.g. Aircall, Chorus, Clari
min_engineering_headcountNoEngineering
min_engineering_open_rolesNoEngineering

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent/no-destruction, and the description adds genuinely new behavioral context: pagination depth (up to 500,000 pages), page size bounds (10–100), and a cost model (2 points per returned record) that matters for a 71-filter search. It does not say whether filters are ANDed or what an empty filter set returns, which is the remaining behavioral gap.

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?

Three front-loaded sentences covering filters, return fields, and paging/cost — high information density with no filler. The paging clause ('paged up to 500,000 pages of 10–100 leads') is slightly compressed but still readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an all-optional, 71-parameter search with no output schema, the description covers the input surface and substitutes for a return schema by naming the fields returned plus cost and paging. Only the combination semantics of filters and the size of an unfiltered result are missing.

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 description coverage is 100%, so the schema already documents all 71 parameters with examples. The description only groups them into families and adds page_size/page bounds, so baseline 3 applies — it adds structure but no meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Find leads') and enumerates the filter families (person, location, company, industry, tech stack, funding, traffic, hiring, HQ), so the agent knows it is a people/lead search rather than a company search. It does not name the nearest siblings (run_linkedin_people_search, run_company_employees), so the boundary is left to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'optionally within one company' hints at a scoping mode, implying you can run broad or narrow, but there is no explicit when-to-use or when-not guidance and no named alternative. Usage is only weakly implied by the filter list.

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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