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mcp-deal-flow-signal

Search Startups by Sector

search_startups_by_sector
Read-onlyIdempotent

Retrieve startups in a specific sector ranked by engineering acceleration. Ideal for investors seeking top companies in verticals like fintech, AI, or cybersecurity.

Instructions

Return every tracked startup within one of 20 supported sectors, ranked by engineering acceleration for the current reporting period.

WHEN TO USE:

  • The user names a specific vertical: 'show me AI/ML startups', 'who's moving in fintech?', 'cybersecurity deal flow', 'climate-tech picks'.

  • You need a focused list for a thesis-driven investor or a sector report.

  • You're comparing momentum inside a defined market before a sourcing cycle.

DO NOT USE FOR:

  • Cross-sector discovery — call get_trending_startups for the top-20 across all sectors.

  • Looking up a named company — call get_startup_signal(name).

  • Discovering which sectors exist or how many startups are tracked overall — call get_signals_summary (it returns live counts and URLs).

  • Multi-sector filtering — the tool accepts exactly one slug per call; issue parallel calls if you need several.

BEHAVIOR:

  • Read-only, idempotent, no side effects.

  • Deterministic within a 7-day window: dataset refreshes every Monday ~09:00 UTC.

  • No authentication required. No rate limit enforced by this server.

  • Returns between 5 and 30 startups per sector depending on open-source density. Dense: ai-ml, developer-tools, data-infrastructure. Sparse: legal-tech, proptech, agtech.

  • On unknown sector slug: returns isError: true with the full list of valid slugs in structuredContent.availableSectors so the agent can retry with a correct value.

  • On upstream failure: returns isError: true with the HTTP status.

  • Open-world: the tracked universe changes week to week.

PARAMETERS:

  • sector (required, string) — MUST be one of the 20 enumerated slugs in inputSchema.properties.sector.enum. Map fuzzy user input BEFORE calling: 'AI' / 'artificial intelligence' / 'ML' → 'ai-ml'; 'crypto' / 'blockchain' → 'web3'; 'cyber' / 'infosec' / 'security' → 'cybersecurity'; 'SaaS' → 'enterprise-saas'; 'devtools' / 'developer experience' → 'developer-tools'; 'climate' / 'clean energy' / 'cleantech' → 'climate-tech'; 'biotech' / 'health' / 'medtech' → 'healthcare'; 'data' / 'databases' → 'data-infrastructure'; 'real estate' → 'proptech'; 'agriculture' → 'agtech'; 'space' → 'space-tech'; 'games' → 'gaming'; 'community' / 'social' → 'social-community'; 'logistics' → 'supply-chain'; 'law' / 'legal' → 'legal-tech'; 'recruiting' / 'HR' → 'hr-tech'; 'learning' / 'education' → 'edtech'; 'commerce' / 'retail infra' → 'ecommerce-infrastructure'; 'hardware' / 'drones' → 'robotics'. If no mapping is clear, call get_signals_summary and ask the user to pick.

RETURNS: { sector: {slug, name, description, url}, period, startupCount, startups[], citation }. Each startup row contains rank, name, sector, stage, geography, commitVelocity14d, commitVelocityChange, contributors, contributorGrowth, newRepos, signalType, description, githubUrl, websiteUrl (when known), linkedinUrl (when known), profileUrl.

TYPICAL WORKFLOW: search_startups_by_sector('fintech') → pick a name → get_startup_signal(name)get_methodology if the user asks what the signal type means.

LIMITATIONS: One sector slug per call; no free-text sector search. For cross-sector views use get_trending_startups. No historical series — each call is the latest weekly snapshot only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectorYesSector slug. Must be one of the 20 supported values. Map fuzzy user input to the closest slug (e.g. 'AI' → 'ai-ml', 'crypto' → 'web3', 'cyber' → 'cybersecurity', 'SaaS' → 'enterprise-saas').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectorYes
periodYes
startupCountYes
startupsYesStartups within the sector, ranked by engineering acceleration.
citationYes

Implementation Reference

  • src/server.ts:299-379 (registration)
    Registration of the 'search_startups_by_sector' tool in the TOOLS array, including its name, description, inputSchema (with sector slug enum), outputSchema, and annotations.
    {
      name: "search_startups_by_sector",
      title: "Search Startups by Sector",
      description: [
        "Return every tracked startup within one of 20 supported sectors, ranked by engineering acceleration for the current reporting period.",
        "",
        "WHEN TO USE:",
        "- The user names a specific vertical: 'show me AI/ML startups', 'who's moving in fintech?', 'cybersecurity deal flow', 'climate-tech picks'.",
        "- You need a focused list for a thesis-driven investor or a sector report.",
        "- You're comparing momentum inside a defined market before a sourcing cycle.",
        "",
        "DO NOT USE FOR:",
        "- Cross-sector discovery — call `get_trending_startups` for the top-20 across all sectors.",
        "- Looking up a named company — call `get_startup_signal(name)`.",
        "- Discovering which sectors exist or how many startups are tracked overall — call `get_signals_summary` (it returns live counts and URLs).",
        "- Multi-sector filtering — the tool accepts exactly one slug per call; issue parallel calls if you need several.",
        "",
        "BEHAVIOR:",
        "- Read-only, idempotent, no side effects.",
        "- Deterministic within a 7-day window: dataset refreshes every Monday ~09:00 UTC.",
        "- No authentication required. No rate limit enforced by this server.",
        "- Returns between 5 and 30 startups per sector depending on open-source density. Dense: ai-ml, developer-tools, data-infrastructure. Sparse: legal-tech, proptech, agtech.",
        "- On unknown sector slug: returns `isError: true` with the full list of valid slugs in `structuredContent.availableSectors` so the agent can retry with a correct value.",
        "- On upstream failure: returns `isError: true` with the HTTP status.",
        "- Open-world: the tracked universe changes week to week.",
        "",
        "PARAMETERS:",
        "- `sector` (required, string) — MUST be one of the 20 enumerated slugs in `inputSchema.properties.sector.enum`. Map fuzzy user input BEFORE calling: 'AI' / 'artificial intelligence' / 'ML' → 'ai-ml'; 'crypto' / 'blockchain' → 'web3'; 'cyber' / 'infosec' / 'security' → 'cybersecurity'; 'SaaS' → 'enterprise-saas'; 'devtools' / 'developer experience' → 'developer-tools'; 'climate' / 'clean energy' / 'cleantech' → 'climate-tech'; 'biotech' / 'health' / 'medtech' → 'healthcare'; 'data' / 'databases' → 'data-infrastructure'; 'real estate' → 'proptech'; 'agriculture' → 'agtech'; 'space' → 'space-tech'; 'games' → 'gaming'; 'community' / 'social' → 'social-community'; 'logistics' → 'supply-chain'; 'law' / 'legal' → 'legal-tech'; 'recruiting' / 'HR' → 'hr-tech'; 'learning' / 'education' → 'edtech'; 'commerce' / 'retail infra' → 'ecommerce-infrastructure'; 'hardware' / 'drones' → 'robotics'. If no mapping is clear, call `get_signals_summary` and ask the user to pick.",
        "",
        "RETURNS: `{ sector: {slug, name, description, url}, period, startupCount, startups[], citation }`. Each startup row contains rank, name, sector, stage, geography, commitVelocity14d, commitVelocityChange, contributors, contributorGrowth, newRepos, signalType, description, githubUrl, websiteUrl (when known), linkedinUrl (when known), profileUrl.",
        "",
        "TYPICAL WORKFLOW: `search_startups_by_sector('fintech')` → pick a name → `get_startup_signal(name)` → `get_methodology` if the user asks what the signal type means.",
        "",
        "LIMITATIONS: One sector slug per call; no free-text sector search. For cross-sector views use `get_trending_startups`. No historical series — each call is the latest weekly snapshot only.",
      ].join("\n"),
      inputSchema: {
        type: "object" as const,
        properties: {
          sector: {
            type: "string",
            description:
              "Sector slug. Must be one of the 20 supported values. Map fuzzy user input to the closest slug (e.g. 'AI' → 'ai-ml', 'crypto' → 'web3', 'cyber' → 'cybersecurity', 'SaaS' → 'enterprise-saas').",
            enum: [...SECTOR_SLUGS],
            examples: ["ai-ml", "fintech", "cybersecurity", "developer-tools"],
          },
        },
        required: ["sector"],
        additionalProperties: false,
      },
      outputSchema: {
        type: "object" as const,
        properties: {
          sector: {
            type: "object",
            properties: {
              slug: { type: "string" },
              name: { type: "string" },
              description: { type: "string" },
              url: { type: "string", format: "uri" },
            },
            required: ["slug", "name"],
          },
          period: { type: "string" },
          startupCount: { type: "integer" },
          startups: {
            type: "array",
            description: "Startups within the sector, ranked by engineering acceleration.",
            items: STARTUP_ITEM_SCHEMA,
          },
          citation: { type: "string" },
        },
        required: ["sector", "period", "startupCount", "startups", "citation"],
      },
      annotations: {
        title: "Search Startups by Sector",
        readOnlyHint: true,
        destructiveHint: false,
        idempotentHint: true,
        openWorldHint: true,
      },
    },
  • Input and output schema for search_startups_by_sector. Input requires a 'sector' string (one of 20 slugs). Output returns sector info, period, startupCount, startups array, and citation.
    inputSchema: {
      type: "object" as const,
      properties: {
        sector: {
          type: "string",
          description:
            "Sector slug. Must be one of the 20 supported values. Map fuzzy user input to the closest slug (e.g. 'AI' → 'ai-ml', 'crypto' → 'web3', 'cyber' → 'cybersecurity', 'SaaS' → 'enterprise-saas').",
          enum: [...SECTOR_SLUGS],
          examples: ["ai-ml", "fintech", "cybersecurity", "developer-tools"],
        },
      },
      required: ["sector"],
      additionalProperties: false,
    },
    outputSchema: {
      type: "object" as const,
      properties: {
        sector: {
          type: "object",
          properties: {
            slug: { type: "string" },
            name: { type: "string" },
            description: { type: "string" },
            url: { type: "string", format: "uri" },
          },
          required: ["slug", "name"],
        },
        period: { type: "string" },
        startupCount: { type: "integer" },
        startups: {
          type: "array",
          description: "Startups within the sector, ranked by engineering acceleration.",
          items: STARTUP_ITEM_SCHEMA,
        },
        citation: { type: "string" },
      },
      required: ["sector", "period", "startupCount", "startups", "citation"],
    },
  • Handler logic for 'search_startups_by_sector' inside the CallToolRequestSchema switch-case. Fetches /api/signals.json, finds the sector by slug, returns error if not found, otherwise maps startups with rank, name, sector, stage, geography, commit velocity, contributors, signalType, etc.
    case "search_startups_by_sector": {
      const sectorSlug = (args as { sector: string }).sector;
      const data = (await fetchJSON("/api/signals.json")) as unknown as SignalsData;
      const sector = data.sectors.find((s) => s.slug === sectorSlug);
      if (!sector) {
        const available = data.sectors.map((s) => s.slug).join(", ");
        return {
          content: [
            {
              type: "text" as const,
              text: `Sector "${sectorSlug}" not found. Available: ${available}`,
            },
          ],
          structuredContent: {
            sector: { slug: sectorSlug, name: "" },
            period: data.meta.period.name,
            startupCount: 0,
            startups: [],
            citation: data.meta.citation,
            error: `Sector "${sectorSlug}" not found.`,
            availableSectors: data.sectors.map((s) => s.slug),
          },
          isError: true,
        };
      }
    
      const structured = {
        sector: {
          slug: sector.slug,
          name: sector.name,
          description: sector.description,
          url: sector.url,
        },
        period: data.meta.period.name,
        startupCount: sector.startups.length,
        startups: sector.startups.map((s, i) => ({
          rank: i + 1,
          name: s.name,
          sector: sector.name,
          stage: s.stage,
          geography: s.geography,
          commitVelocity14d: s.commitVelocity14d,
          commitVelocityChange: s.commitVelocityChange,
          contributors: s.contributors,
          contributorGrowth: s.contributorGrowth,
          newRepos: s.newRepos,
          signalType: s.signalType,
          description: s.description,
          githubUrl: s.githubUrl,
          ...(s.websiteUrl ? { websiteUrl: s.websiteUrl } : {}),
          ...(s.linkedinUrl ? { linkedinUrl: s.linkedinUrl } : {}),
          profileUrl: s.profileUrl,
        })),
        citation: data.meta.citation,
      };
    
      const lines = sector.startups.map(
        (s, i) =>
          `${i + 1}. ${s.name} — ${s.commitVelocityChange} velocity change, ${s.contributors} contributors, signal: ${s.signalType}\n   ${s.description || "(no description)"}`
      );
      return {
        content: [
          {
            type: "text" as const,
            text: `${sector.name} Startups (${data.meta.period.name})\n${sector.description}\n${sector.startups.length} startups tracked\n\n${lines.join("\n\n")}\n\nSource: ${BASE_URL}/startups-to-watch/${sectorSlug}-q2-2026\nCitation: ${data.meta.citation}\n\n${FOOTER}`,
          },
        ],
        structuredContent: structured,
      };
    }
  • SECTOR_SLUGS constant array defining the 20 supported sector slugs used in the input schema enum and validation.
    const SECTOR_SLUGS = [
      "ai-ml",
      "fintech",
      "cybersecurity",
      "developer-tools",
      "healthcare",
      "climate-tech",
      "enterprise-saas",
      "data-infrastructure",
      "web3",
      "robotics",
      "edtech",
      "ecommerce-infrastructure",
      "supply-chain",
      "legal-tech",
      "hr-tech",
      "proptech",
      "agtech",
      "gaming",
      "space-tech",
      "social-community",
    ] as const;
  • Startup and Sector interfaces used as data structures for the handler logic.
    interface Startup {
      name: string;
      description: string;
      stage: string;
      geography: string;
      commitVelocity14d: number;
      commitVelocityChange: string;
      contributors: number;
      contributorGrowth: string;
      newRepos: number;
      signalType: string;
      githubUrl: string;
      websiteUrl?: string;
      linkedinUrl?: string;
      profileUrl?: string;
    }
    
    interface Sector {
      slug: string;
      name: string;
      description: string;
      startupCount: number;
      startups: Startup[];
      url: string;
    }
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description adds critical behavioral details: deterministic within a 7-day window, no auth required, no rate limit, return count range (5-30), error handling for unknown and upstream failures, and open-world dynamic updates. No contradiction with annotations.

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 lengthy but well-structured with clear sections and front-loaded purpose. Each section serves a purpose; no redundant fluff. Slightly verbose but appropriate for the complexity of the tool; scores a 4 rather than 5 due to minor redundancy (e.g., repeating 'one slug per call' in multiple places).

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?

Given the single parameter with full schema coverage, strong annotations, and implied output schema, the description covers all necessary aspects: purpose, usage, behavior, parameter semantics, error handling, limitations, and typical workflow. It leaves no gaps for an AI agent to misunderstand the tool's capabilities.

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 100% and includes description and enum. The description massively adds value by providing a comprehensive fuzzy-to-slug mapping table, examples, and the constraint that exactly one slug is accepted per call. This compensates beyond the schema's own documentation.

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?

Description explicitly states the function: 'Return every tracked startup within one of 20 supported sectors, ranked by engineering acceleration for the current reporting period.' The verb is specific, the resource is clear, and the scope is delineated. The 'DO NOT USE FOR' section further distinguishes it from sibling tools like get_trending_startups and get_startup_signal.

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?

Contains dedicated 'WHEN TO USE' and 'DO NOT USE FOR' sections with explicit conditions, alternative tool names, and a typical workflow example. This provides clear guidance on when to invoke this tool versus others such as get_trending_startups or get_signals_summary.

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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