Skip to main content
Glama
Sign In

RocketList

by ai.rocketlist
OverviewDiscussionsAdmin

Server Details

Search current startup jobs and hiring companies through RocketList's public read-only data.

If you are the author of this connector, you can claim ownership by verifying the domain or GitHub account it belongs to. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
Status
Healthy
Uptime
99.9% over 44 days
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL
Repository
rocketlist-ai/startup-jobs
GitHub Stars
0
ChatGPT
–
Claude
–

TDQS

A4.5/5.0

Scored across 5 tools

Disambiguation5/5

Each tool targets a clearly distinct resource or action: get_company and get_job are single-record lookups by identifier, search_companies and search_jobs are list queries on separate entities, and hiring_stats is the only aggregation tool. The descriptions explicitly steer users from hiring_stats to search_jobs for role listings, eliminating the only plausible overlap.

Naming Consistency4/5

Four tools follow a clean verb_noun pattern (get_company, get_job, search_companies, search_jobs), while hiring_stats is a noun phrase with no verb. This is a minor deviation that does not impede readability or prediction.

Tool Count5/5

Five tools are well-scoped for a public hiring-data API: two detail lookups, two searches, and one aggregate stats endpoint. Each tool earns its place, and no redundant or trivial tools are present.

Completeness4/5

The surface covers discovery (search_jobs, search_companies), detail retrieval (get_job, get_company), and analytics (hiring_stats), which is most of the needed lifecycle for a read-only public dataset. Minor gaps exist, such as no direct way to retrieve the full set of filter values (e.g., all domains, stages, or investors) without running an aggregate query.

Available Tools

5 tools
get_companyGet company detailsA
Read-onlyIdempotent
Inspect

Get public details and recent active jobs for one hiring company by UUID.

NameRequiredDescriptionDefault
company_idYes

Output Schema

NameRequiredDescription

No output parameters

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful context by noting the data is 'public' and includes 'recent active jobs', which gives the agent a sense of access level and content scope beyond 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?

The description is one compact sentence with no filler. The key scoping detail ('by UUID') and the expected content ('public details and recent active jobs') are included without redundancy.

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 simple read-only tool with one parameter, an output schema, and safety annotations, the description is complete. It states what is returned, how the company is identified, and the public nature of the data. No critical invocation details are missing.

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 0%, so the description must compensate. It does by specifying that company_id is a UUID, which is a meaningful semantic addition over the schema's bare 'string' type. With a single required parameter, this is sufficient for correct invocation.

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 uses a specific verb ('Get') and resource ('public details and recent active jobs for one hiring company'), and identifies the key identifier as UUID. This clearly distinguishes it from sibling search tools like search_companies and search_jobs.

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?

The description clearly indicates the tool is for fetching a single company by UUID, which implies it is not for searching. It does not explicitly name alternatives or exclusion conditions, but the scope is clear enough for an agent to select it appropriately.

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

get_jobGet job detailsA
Read-onlyIdempotent
Inspect

Get public details for one active Rocketlist job by slug or UUID.

NameRequiredDescriptionDefault
job_idYes

Output Schema

NameRequiredDescription

No output parameters

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds 'public' and 'active' scope but does not disclose additional behavior such as error conditions when the job is not found or inactive. This is adequate but not rich.

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?

A single, front-loaded sentence with no filler. Every element—scope, resource, and identifier format—earns its place.

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 single-parameter read operation with an output schema and complete annotations, the description covers everything needed to invoke the tool correctly. No additional return-value or safety documentation is necessary.

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?

The input schema only says job_id is a required string with no description. The description compensates by explaining that job_id accepts either a slug or a UUID, which is meaningful semantic information beyond the schema.

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 states a specific verb ('Get'), a precise resource ('one active Rocketlist job'), and the exact lookup keys ('by slug or UUID'). It clearly distinguishes this tool from the search-oriented siblings like search_jobs.

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?

The description makes the use case clear: retrieve a single known active job by slug or UUID. It does not explicitly name alternatives or exclusions, but the contrast with search_jobs is strongly implied by 'one active ... by slug or UUID.'

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

hiring_statsHiring statisticsA
Read-onlyIdempotent
Inspect

Aggregate counts over ALL matching public Rocketlist jobs, not a sample: total jobs and companies, the top values by city, country, company, domain (vertical), company stage, work mode (remote/hybrid/on-site), job category and seniority, and published pay with sample sizes. Use it for 'how many', 'which cities/companies hire most', share and pay questions; use search_jobs for the roles themselves.

Questions the Startup Hiring Map can answer (at most one city or country, optionally domain and remote_only) are counted from the same snapshot as getedge.cc/map, so totals match the map exactly (job location; a multi-city role counts in each city). Other filters use the live catalog; the result's source says which.

Takes the same filters as search_jobs except skills, plus:

  • group_by: subset of ['city', 'country', 'company', 'domain', 'stage', 'work_mode', 'category', 'seniority'] (default: all).

  • top: values listed per dimension, 1-50 (default 10).

  • query: free-text over job title, company, location and category. Matched as prefix tokens, so 'engineer' also matches 'Engineering'.

  • domain: exact company industry/vertical, e.g. ['Fintech'].

  • city: substring of the job's city or country.

  • remote_only: keep only remote roles.

  • investor: companies backed by these investors, e.g. ['Index Ventures']. Common aliases resolve ('a16z' finds Andreessen Horowitz). Populated for about 83% of hiring companies, so a company with no recorded investors is never returned when this is set.

  • stage: exact company funding stage from ['Pre-Seed', 'Seed', 'Series A', 'Series B', 'Series C', 'Series D', 'Series D+', 'Series E', 'Series F', 'Series G', 'Growth', 'Public', 'Acquired', 'Bootstrapped'], case-insensitive. About 12% of hiring companies have no stage recorded and are excluded when this is set.

  • min_experience_years / max_experience_years: bounds on the years of experience the role requires at minimum. max_experience_years=5 means 'requires 5 years or fewer'. Only about 56% of jobs state a requirement; the rest are excluded when either bound is set.

  • yc_batch: Y Combinator batches, e.g. ['F26'] or ['Fall 2026'] (W/X/S/F + year). Covers the batches in the reviewed cohort list (currently YC Fall 2026).

  • founder_origin: countries the founders come from (where they grew up or studied), by name or ISO code, e.g. ['Germany'] or ['DE']. Company-level, reviewed for the same cohorts as yc_batch; a company matches if any founder does.

  • near: a place, as an address, a street corner ('3rd and Folsom, San Francisco') or 'lat,lng'. Keeps companies whose office (the reviewed street address on getedge.cc/map) is within radius_m metres (default 1000) or walk_minutes on foot (64 m of straight line per minute). Results carry office_address, distance_m and walk_minutes, nearest first.

  • radius_m / walk_minutes: size of the near area; give one.

NameRequiredDescriptionDefault
topNo
cityNo
nearNo
queryNo
stageNo
domainNo
group_byNo
investorNo
radius_mNo
yc_batchNo
remote_onlyNo
walk_minutesNo
founder_originNo
max_experience_yearsNo
min_experience_yearsNo

Output Schema

NameRequiredDescription

No output parameters

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare the tool is read-only, idempotent, and non-destructive. The description adds substantial behavioral context beyond that: snapshot versus live-catalog behavior, the `source` field, exclusion rules for missing investor/stage/experience data, and coverage percentages that materially affect 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 long, but it is front-loaded with purpose and usage before moving into detailed parameter semantics. The density is justified by a 15-parameter tool, though the long list of caveats and parameter notes could be slightly tightened without losing meaning.

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 complex aggregation tool with 15 optional parameters, an output schema, and no schema descriptions, this definition is complete enough to invoke correctly. It explains what is counted, which filters are supported, how missing-data exclusions work, and what special result fields like `source`, `office_address`, `distance_m`, and `walk_minutes` mean.

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 description coverage is 0%, so the description carries the full burden and does so comprehensively for all 15 parameters. It defines exact semantics for stage, domain, city, remote_only, investor aliases, experience bounds, yc_batch, founder_origin, near, radius_m, walk_minutes, group_by, and top, including defaults and edge-case behavior.

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 states a specific verb and resource: aggregate counts over ALL matching public Rocketlist jobs, with named breakdown dimensions. It explicitly distinguishes itself from search_jobs, which returns the roles themselves, so the agent can choose 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?

It gives explicit selection guidance: use for 'how many', 'which cities/companies hire most', share and pay questions, and use search_jobs for the roles themselves. It also explains the special Startup Hiring Map subset and when the result is counted from the same snapshot versus the live catalog.

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

search_companiesSearch hiring companiesA
Read-onlyIdempotent
Inspect

Search public Rocketlist hiring companies. Returns bounded, non-personal company data only.

Only companies with at least one active job are returned, matching the company list on rocketlist.ai.

All list arguments are arrays of strings; passing a bare string is a validation error.

Filters:

  • query: substring of the company name or slug, or a whole sector word matching the company's industry, vertical or subvertical. 'fintech' finds companies whose industry is FinTech; 'acme' finds Acme Robotics by name. Sector matching is by whole word, so 'ai' matches the AI & ML vertical and not Blockchain.

  • investor: companies backed by these investors, e.g. ['Index Ventures']. Common aliases resolve ('a16z' finds Andreessen Horowitz). Populated for about 83% of hiring companies, so a company with no recorded investors is never returned when this is set.

  • stage: exact company funding stage from ['Pre-Seed', 'Seed', 'Series A', 'Series B', 'Series C', 'Series D', 'Series D+', 'Series E', 'Series F', 'Series G', 'Growth', 'Public', 'Acquired', 'Bootstrapped'], case-insensitive. About 12% of hiring companies have no stage recorded and are excluded when this is set.

  • yc_batch: Y Combinator batches, e.g. ['F26'] or ['Fall 2026'] (W/X/S/F + year). Covers the batches in the reviewed cohort list (currently YC Fall 2026).

  • founder_origin: countries the founders come from (where they grew up or studied), by name or ISO code, e.g. ['Germany'] or ['DE']. Company-level, reviewed for the same cohorts as yc_batch; a company matches if any founder does.

  • near: a place, as an address, a street corner ('3rd and Folsom, San Francisco') or 'lat,lng'. Keeps companies whose office (the reviewed street address on getedge.cc/map) is within radius_m metres (default 1000) or walk_minutes on foot (64 m of straight line per minute). Results carry office_address, distance_m and walk_minutes, nearest first.

  • radius_m / walk_minutes: size of the near area; give one.

NameRequiredDescriptionDefault
nearNo
limitNo
queryNo
stageNo
investorNo
radius_mNo
yc_batchNo
walk_minutesNo
founder_originNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent/non-destructive, so the description is free to add what they can't: data is bounded and non-personal, only active-job companies are included, coverage is partial (~83% investor data, ~12% missing stage), and matching semantics are disclosed (whole-word sector matching so 'ai' doesn't hit Blockchain). These are exactly the behaviors that change how an agent interprets an empty or partial result set.

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-loaded with scope and the active-job constraint, then a clean bulleted filter breakdown where each bullet earns its place. It is long, but proportionate to nine undocumented parameters; the only slight waste is the duplicated 'whole word' sector explanation.

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?

An output schema exists, so explaining return values isn't required; the description instead notes the extra fields near-queries add (office_address, distance_m, walk_minutes) and the nearest-first ordering. With nine params at 0% schema coverage, this level of detail is what makes the tool callable correctly.

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 0%, so the description carries the full burden and does so with concrete examples and edge cases: 'fintech' vs 'acme', alias resolution for a16z, the enumerated stage vocabulary, YC batch formats ('F26' or 'Fall 2026'), ISO codes for founder_origin, and three accepted forms for near. It even flags the array-of-strings validation error and the radius_m/walk_minutes either-or rule.

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 ("Search public Rocketlist hiring companies") plus the defining scope constraint: only companies with at least one active job, matching the rocketlist.ai list. This clearly separates it from search_jobs (job records) and get_company (single-entity lookup) without needing to name them.

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?

The filter-by-filter semantics imply when each filter applies, and the exclusion caveats (no investors recorded = never returned; no stage = excluded) tell the agent when results will be sparse. What's missing is explicit routing guidance versus siblings, e.g. "use get_company for full detail" or "use search_jobs to find roles."

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

search_jobsSearch jobsA
Read-onlyIdempotent
Inspect

Search public active Rocketlist jobs. Returns bounded, non-personal job data only.

All list arguments are arrays of strings; passing a bare string is a validation error.

Filters:

  • query: free-text over job title, company, location and category. Matched as prefix tokens, so 'engineer' also matches 'Engineering'.

  • skills: matched against the job's listed tech stack.

  • domain: exact company industry/vertical, e.g. ['Fintech'].

  • city: substring of the job's city or country.

  • remote_only: keep only remote roles.

  • investor: companies backed by these investors, e.g. ['Index Ventures']. Common aliases resolve ('a16z' finds Andreessen Horowitz). Populated for about 83% of hiring companies, so a company with no recorded investors is never returned when this is set.

  • stage: exact company funding stage from ['Pre-Seed', 'Seed', 'Series A', 'Series B', 'Series C', 'Series D', 'Series D+', 'Series E', 'Series F', 'Series G', 'Growth', 'Public', 'Acquired', 'Bootstrapped'], case-insensitive. About 12% of hiring companies have no stage recorded and are excluded when this is set.

  • min_experience_years / max_experience_years: bounds on the years of experience the role requires at minimum. max_experience_years=5 means 'requires 5 years or fewer'. Only about 56% of jobs state a requirement; the rest are excluded when either bound is set.

  • yc_batch: Y Combinator batches, e.g. ['F26'] or ['Fall 2026'] (W/X/S/F + year). Covers the batches in the reviewed cohort list (currently YC Fall 2026).

  • founder_origin: countries the founders come from (where they grew up or studied), by name or ISO code, e.g. ['Germany'] or ['DE']. Company-level, reviewed for the same cohorts as yc_batch; a company matches if any founder does.

  • near: a place, as an address, a street corner ('3rd and Folsom, San Francisco') or 'lat,lng'. Keeps companies whose office (the reviewed street address on getedge.cc/map) is within radius_m metres (default 1000) or walk_minutes on foot (64 m of straight line per minute). Results carry office_address, distance_m and walk_minutes, nearest first.

  • radius_m / walk_minutes: size of the near area; give one.

NameRequiredDescriptionDefault
cityNo
nearNo
limitNo
queryNo
stageNo
domainNo
skillsNo
investorNo
radius_mNo
yc_batchNo
remote_onlyNo
walk_minutesNo
founder_originNo
max_experience_yearsNo
min_experience_yearsNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already cover the read-only, idempotent, non-destructive profile, and the description goes well beyond them: it warns that bare-string list arguments are a validation error, and discloses data-coverage exclusions (investor populated ~83%, stage ~12% missing, experience stated ~56%) so the agent knows filters silently drop jobs. It also states the near results are ordered nearest-first and carry office_address, distance_m and walk_minutes.

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-loaded with scope, then a compact bulleted filter list where each entry earns its place — the coverage percentages and aliasing notes are the kind of detail an agent cannot get elsewhere. It runs long, but padding is minimal; the only dead weight is the unexplained omission of limit alongside verbose percent phrasings.

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?

Fifteen optional parameters with no schema descriptions demand heavy prose, and the description delivers for nearly all of them; return shape is covered by the output schema. Missing pieces are minor: no mention of limit/pagination behavior or what a result set's overall shape is when near is not set.

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 0%, so the description carries the full burden and does it: prefix-token matching for query ('engineer' matches 'Engineering'), skills vs domain semantics, investor alias resolution ('a16z'), the exact stage enumeration, and the counterintuitive reading of max_experience_years='requires 5 years or fewer'. Only limit goes undocumented.

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?

Opening line names a specific verb and resource ('Search public active Rocketlist jobs') and immediately scopes the corpus ('bounded, non-personal job data only'), which separates it from get_job and search_companies without the reader needing 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 Guidelines3/5

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

The bullet list explains how each filter behaves, which strongly implies how to compose a query, but it never states when to use this tool rather than search_companies or get_job, and gives no explicit exclusions or prerequisite conditions. Usage is inferable rather than stated.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updates
    • Changedhiring_stats5 fields changed
      • addedInput schema / properties / founder_origin
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Founder Origin"
+}
      • addedInput schema / properties / near
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Near"
+}
      • addedInput schema / properties / radius_m
        Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Radius M"
+}
      • addedInput schema / properties / walk_minutes
        Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Walk Minutes"
+}
      • addedInput schema / properties / yc_batch
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Yc Batch"
+}
    • Changedsearch_companies5 fields changed
      • addedInput schema / properties / founder_origin
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Founder Origin"
+}
      • addedInput schema / properties / near
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Near"
+}
      • addedInput schema / properties / radius_m
        Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Radius M"
+}
      • addedInput schema / properties / walk_minutes
        Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Walk Minutes"
+}
      • addedInput schema / properties / yc_batch
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Yc Batch"
+}
    • Changedsearch_jobs5 fields changed
      • addedInput schema / properties / founder_origin
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Founder Origin"
+}
      • addedInput schema / properties / near
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Near"
+}
      • addedInput schema / properties / radius_m
        Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Radius M"
+}
      • addedInput schema / properties / walk_minutes
        Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Walk Minutes"
+}
      • addedInput schema / properties / yc_batch
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Yc Batch"
+}
  2. 1 tool update
    • Addedhiring_stats
  3. 4 tool updates
    • First observedget_company
    • First observedget_job
    • First observedsearch_companies
    • First observedsearch_jobs

Related MCP Connectors

Related MCP Servers

  • Startup Jobs MCP Server

    killbridge
    A
    license
    Not graded
    quality
    D
    maintenance
    Search live startup.jobs listings with filters for role, location, and employment type, plus get job details, company profiles, hiring trends, and salary benchmarks.
    MIT

  • simplyapply-jobs-mcp

    tsushanth
    A
    license
    A
    quality
    C
    maintenance
    Enables real-time job search across thousands of companies' open roles from Greenhouse, Lever, Ashby, and SmartRecruiters, with full-text filtering and company-specific queries, no API key required.
    2
    MIT
  • LAYIQ Jobs

    LAYIQ Jobsofficial

    roleworthylabs
    A
    license
    Not graded
    quality
    C
    maintenance
    Enables users to search public job listings and retrieve details for individual postings from inside AI assistants, including ranked results, filters, and pagination. It is read-only and requires no account, authentication, or API key.
    MIT
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