startup-gtm-skill
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| dataset_summaryA | Orientation call: how many companies, split by status/era/confidence/sector. Cheap. Use this first to understand what the dataset covers before querying. |
| list_filtersA | Return the valid values for every filter accepted by find_channels (sector groups, business models, eras, statuses, trust/virality/CAC/category levels, stages, and the 24 canonical channels). Call this if unsure what to pass. |
| find_channelsA | PRIMARY TOOL. Given a marketing situation, return the channels comparable Indian startups actually used — ranked by prevalence and split across growth stages (0→1, 1→10, scale), with named example companies as evidence. Pass any subset of: sector (a group like 'Fintech' or a raw sector), business_model (B2C/B2B/D2C/SaaS/marketplace/B2B2C), era ('2010-2016', '2016-2019', '2020-2021', '2022-2025'), status, trust_burden (low/medium/high), virality (low/medium/high), cac_ceiling (low/medium/high/enterprise), category_play (creation/capture). Optionally set stage to rank by that stage. Always report the returned cohort_size and honor any 'warning' about small cohorts. |
| get_companyA | Full channel profile for one company (fuzzy name match): channels by stage, what worked, what failed, economics, sponsorships, variables, confidence, sources. |
| search_evidenceA | Keyword search across the narrative evidence (what worked / what failed / signature moves / ignition). Use for 'when did X fail', 'who used Y for Z'. Returns short snippets with company names — not full rows. |
| channel_lensB | Inverse view for one channel: how common it is, its stage split, which sectors use it most, and the companies behind it. Accepts a partial channel name. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool serves a distinct purpose: dataset_summary orients, list_filters enumerates valid inputs, find_channels is the primary search, channel_lens provides the inverse view, get_company gives deep company profiles, and search_evidence does keyword-level narrative lookup. There is no functional overlap.
All names are lowercase snake_case, but the pattern is mixed: most tools start with a verb (list, find, get, search), while two are noun phrases (channel_lens, dataset_summary). This is a minor deviation that doesn't harm usability.
Six tools is well-scoped for a specialized GTM research skill. Each tool earns its place and together they cover orientation, discovery, deep dive, and evidence search without redundancy.
The domain is read-only GTM channel analysis. The set covers the full workflow: understand the dataset, know valid filters, find channels, inspect a channel in reverse, get company detail, and search evidence. No critical operations are missing.