LinkedIn data for AI agents: structured profiles, people search, companies, and posts over MCP or REST. 500 free credits, no card.
Scored across 7 tools
Each tool targets a distinct LinkedIn data type or action: company, profile, posts, URL resolve, account usage, account creation, and people search. No two tools overlap in purpose, so an agent can clearly distinguish them.
Tool names follow a consistent verb_noun pattern using snake_case (get_*, provision, resolve_url, search_people). The get_ prefix is uniformly used for data retrieval, and other verbs are clear and predictable.
With 7 tools, the server is well-scoped for its purpose of providing LinkedIn people and company data. Each tool serves a distinct function without unnecessary duplication, and the count feels right for a focused API.
The tool set covers core operations: fetching company, profile, posts, searching people, account management, and URL resolution. However, search for companies by name lacks a direct tool (use people search with company filter) and there is no post search or single post retrieval, but these are acceptable for a v1.
Check your balance, plan, limits, and the last 10 charges (receipt ids included). Costs 0 credits and is exempt from the per-minute rate limit, so call it first to check your budget before running data tools, and any time after. Works the same on a trial key, a paid key, or an OAuth-connected account. The response includes upgrade_url (give it to your human when credits or plan limits block you; purchases credit this account directly with no login) and manage_url (give it to your human to change or cancel a paid plan in the Stripe billing portal). Trial accounts also get a claim_url that attaches an email so the account can be recovered if the key is lost. Not for fetching platform data.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| usage | Yes | |
| common | Yes | |
| entity | Yes | |
| platform | Yes | |
| freshness | Yes | |
| data_as_of | Yes | |
| canonical_url | Yes | |
| schema_version | Yes | |
| platform_fields | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses zero cost, rate limit exemption, consistent behavior across account types, and includes URLs for upgrade, manage, and account recovery. No contradiction with annotations (readOnlyHint=true, openWorldHint=false).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
All sentences are informative and front-loaded with key purpose. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With zero parameters and an output schema, the description fully covers usage context, behavioral notes, and key response fields (URLs).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters needed; baseline is 4. Description adds context about returned URLs and account-specific behavior, exceeding baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it checks balances, plan, limits, and last 10 charges. Differentiates from LinkedIn-specific sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises calling first for budgeting before running data tools, and confirms it works on any account type. Also clarifies it's not for fetching platform data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch one company's LinkedIn page: name, description, industry, employee count, headquarters, website, founding year, specialities, and the URN/numeric id you need for linkedin_search_people company filters. identifier accepts a company URL, the slug after /company/ (e.g. 'microsoft'), or a website domain like 'microsoft.com'; numeric ids and URNs are search-filter inputs, not fetch identifiers. Domains are resolved to a company and verified against that company's website: a domain identifier always QUOTES base+4 credits (set max_credits accordingly), and the 4-credit resolution surcharge is refunded at settlement when the domain was resolved before, so known domains settle at the base price. A domain that cannot be verified to a company returns INVALID_INPUT with the closest matches instead of a guessed company. Costs 4 credits base. Do not guess a slug from a brand name: slugs are vanity strings and a famous name can belong to an unrelated company's page (linkedin.com/company/anthropic is a small investment fund, not the AI lab). When you only know the company's name, pass its website domain instead -- the verified form -- and sanity-check the returned industry and description against what you expected. This tool does not search by name: if you only have an approximate company name, use linkedin_search_people's current_company filter with keywords (names are matched natively there) or give the exact slug. For the company's posts, use linkedin_get_posts with the same identifier (URL, slug, or website domain all work there too).
| Name | Required | Description | Default |
|---|---|---|---|
| freshness | No | recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data). Trial keys are cached-only and reject realtime with TRIAL_CAP_EXCEEDED; paying upgrades this same key to unlock it. | recent |
| identifier | Yes | Company URL, slug (after /company/), or website domain (e.g. 'microsoft.com'). Numeric ids/URNs are not fetchable; use them only in linkedin_search_people company filters. | |
| max_credits | No | Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling. |
| Name | Required | Description |
|---|---|---|
| usage | Yes | |
| common | Yes | |
| entity | Yes | |
| platform | Yes | |
| freshness | Yes | |
| data_as_of | Yes | |
| canonical_url | Yes | |
| schema_version | Yes | |
| platform_fields | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and openWorldHint. The description adds critical behavioral details: domain resolution surcharge and refund, error handling for unverified domains, trial key limitations on freshness, and the non-fetchability of numeric IDs. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative but somewhat lengthy with multiple paragraphs. While every sentence adds value, a more streamlined structure could improve readability. Still well-organized with clear warnings and usage notes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, output schema exists), the description thoroughly covers all aspects: input validation, error states, credit costs, alternatives, and expected return data. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the description adds significant value: it explains valid identifier types (URL, slug, domain), warns against numeric IDs, clarifies freshness modes and trial restrictions, and describes max_credits behavior. This goes well beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a company's LinkedIn page and lists the fields returned. It distinguishes from siblings by mentioning linkedin_get_posts for posts and linkedin_search_people for name-based searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives, including handling approximate names, slug guessing, and domain verification. It also explains credit costs and behaviors.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the recent LinkedIn posts of one person or one company. identifier accepts a profile or company URL, a slug, a person URN, or a company website domain like 'microsoft.com'; the entity type is detected automatically. A domain resolves to its verified company first, exactly like linkedin_get_company: it QUOTES base+4 credits (set max_credits accordingly) and the surcharge is refunded at settlement for already-known domains, so they settle at the base price. Company URNs and numeric company ids are search-filter inputs, not fetch identifiers: use the company slug, URL, or domain here. Returns one page of posts (text, created_at, author, likes, comments_count, shares, is_repost, url) with a cursor for older posts. Costs 4 credits per page. Use this for 'what has X been posting', voice-of-company research, or activity checks before outreach. Not for reading one specific post you already have a URL for, and not for keyword search across LinkedIn; neither is supported in v1.
| Name | Required | Description | Default |
|---|---|---|---|
| cursor | No | Cursor from a previous page for older posts. | |
| freshness | No | recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data). Trial keys are cached-only and reject realtime with TRIAL_CAP_EXCEEDED; paying upgrades this same key to unlock it. | recent |
| identifier | Yes | Person or company URL, slug, URN, or company website domain. | |
| max_credits | No | Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling. |
| Name | Required | Description |
|---|---|---|
| usage | Yes | |
| common | Yes | |
| entity | Yes | |
| platform | Yes | |
| freshness | Yes | |
| data_as_of | Yes | |
| canonical_url | Yes | |
| schema_version | Yes | |
| platform_fields | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description extensively discloses behaviors beyond annotations, including pricing (4 credits per page, surcharge for domains, refund), caching behavior (recent vs realtime), and identifier auto-detection. No contradiction with readOnlyHint annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise, front-loaded with core purpose, then detailed parameters and usage. Every sentence serves a purpose, though slightly verbose due to pricing details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is comprehensive, covering all aspects: purpose, identifier types, pricing, caching, pagination, and usage guidance. Output schema handles return values, making the description complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant context beyond schema, such as how identifier resolves domains, surcharge refund logic, and max_credits function. This enhances understanding of parameter behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches recent LinkedIn posts for one person or company, using specific verbs and resource. It distinguishes from siblings by explicitly stating it is not for reading a single post or keyword search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use (e.g., 'what has X been posting', voice-of-company research, activity checks) and when not to use (not for specific post or keyword search). It does not name alternative sibling tools but provides clear usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch one person's LinkedIn profile. identifier accepts a profile URL, the slug after /in/ (e.g. 'williamhgates'), or a urn:li:fsd_profile URN; URLs are cleaned automatically. Always returns the overview (name, headline, location, current position, follower counts) plus up to 2 requested sections from about|experience|education|skills at no extra cost; each section beyond 2 adds 2 credits (max 4 sections). Costs 4 credits base. If you only have a name, use linkedin_search_people first; this tool does not search. Results from linkedin_search_people with is_anonymous=true cannot be fetched here; treat them as 'someone matching this exists' and stop. Companies belong to linkedin_get_company.
| Name | Required | Description | Default |
|---|---|---|---|
| sections | No | Extra profile sections. First 2 are included in the base price. | |
| freshness | No | recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data). Trial keys are cached-only and reject realtime with TRIAL_CAP_EXCEEDED; paying upgrades this same key to unlock it. | recent |
| identifier | Yes | Profile URL, slug (after /in/), or urn:li:fsd_profile URN. | |
| max_credits | No | Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling. |
| Name | Required | Description |
|---|---|---|
| usage | Yes | |
| common | Yes | |
| entity | Yes | |
| platform | Yes | |
| freshness | Yes | |
| data_as_of | Yes | |
| canonical_url | Yes | |
| schema_version | Yes | |
| platform_fields | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost structure (4 credits base + 2 per extra section beyond 2), caching behavior (recent/realtime), trial key limitations, and credit refund policy. Annotations (readOnlyHint, openWorldHint) are supported and not contradicted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is comprehensive yet efficient; each sentence adds information. Minor redundancy in listing sections again, but overall well-structured with front-loaded main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Combined with full schema coverage, annotations, and output schema (implied), the description covers all necessary context: identifier types, cost, caching, limits, alternatives, and failure conditions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers all 4 parameters with descriptions (100% coverage). The description adds value by explaining sections cost, freshness credit implications, and max_credits as a rejection threshold, enriching beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Fetch one person's LinkedIn profile' and specifies three accepted identifier formats (URL, slug, URN). It differentiates from siblings by directing name-based lookups to linkedin_search_people and company profiles to linkedin_get_company.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises when to use alternative tools: 'If you only have a name, use linkedin_search_people first' and 'Companies belong to linkedin_get_company.' Also explains that anonymous search results cannot be fetched, and provides conditions for realtime vs cached fetches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify what a LinkedIn URL points at before fetching it. Give any LinkedIn profile, company, or post URL (utm params, www/m subdomains, trailing slashes are fine); get back {type: person|company|post, id, handle, canonical_url}. For profile URLs, id is the stable person URN; for company URLs, id is the stable company URN; for post URLs, id is the activity URN extracted from the URL. For people, use the returned handle or id with linkedin_get_profile or linkedin_get_posts. For companies, use the returned HANDLE with linkedin_get_company or linkedin_get_posts; the company URN/id is a linkedin_search_people filter input, not a fetch identifier. Costs 2 credits. Skip this tool when you already have a slug, URN, or clean URL: linkedin_get_profile and linkedin_get_company accept those directly, so resolving first would waste 2 credits. Not for non-LinkedIn URLs; it returns INVALID_INPUT for those.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | A LinkedIn URL, e.g. https://www.linkedin.com/in/williamhgates or .../company/microsoft. |
| Name | Required | Description |
|---|---|---|
| usage | Yes | |
| common | Yes | |
| entity | Yes | |
| platform | Yes | |
| freshness | Yes | |
| data_as_of | Yes | |
| canonical_url | Yes | |
| schema_version | Yes | |
| platform_fields | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond readOnlyHint=true, description adds credit cost, details of returned fields (id, handle, canonical_url), error for non-LinkedIn URLs, and specific URN extraction logic. No contradictions 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with front-loaded purpose, but the output format details make it slightly verbose. Still reasonably concise and organized, earning a 4.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given output schema exists, description sufficiently explains return values, cost, error handling, and integration with sibling tools. No gaps for a resolve tool with clear annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear description for the url parameter. Description adds extra context: supports utm params, subdomains, trailing slashes, and gives examples, exceeding schema baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it identifies what a LinkedIn URL points at, with specific verb 'resolve' and resource 'LinkedIn URL'. It distinguishes from siblings like linkedin_get_profile and linkedin_get_company by explaining when to use this tool first.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use (before fetching a LinkedIn URL) and when not to use (when already have slug/URN/clean URL, as direct tools accept those). Names alternative tools and warns about credit cost, providing clear decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find people on LinkedIn by keywords and filters. The right tool when you have a name, role, or 'who is the X at Y' question without a profile URL. Pass keywords (free text: name, title, or both) and any of first_name, last_name, title, school, current_company, past_company. If keywords is omitted it is derived from the name or title filters; school or company filters alone are rejected with INVALID_INPUT, so include keywords with those. Company filters accept a company name, slug, numeric id, or URN. current_company names are matched by LinkedIn's own company search: typo-tolerant and fuzzy, so results can include people whose headline merely mentions the company; when you need exact filtering, pass the numeric id or URN (linkedin_get_company returns both). past_company names are resolved to an id for you. Costs 10 credits including the first 10 results; each further 10 results add 1 credit (limit max 30; trial keys max 10). A cursor page is a NEW call priced the same way by its own limit, so one limit=30 call is much cheaper than three limit=10 pages; prefer a larger limit over paginating. Do NOT combine a past_company NAME or a company-URL filter with limit=30: resolving those spends one of the call's three internal fetches, so that combination is rejected; keep limit<=20 with them or pass the numeric id. current_company names never spend a fetch, so they combine with any limit. Returns name, position, location, urn, public_identifier per result, a cursor for the next page, and total_matches. Results with is_anonymous=true are private profiles; do not pass them to linkedin_get_profile. For one known person with a URL/slug, call linkedin_get_profile directly instead.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many results to return. | |
| title | No | Current job title filter. | |
| cursor | No | Cursor from a previous page. | |
| school | No | School or university name filter. | |
| keywords | No | Free-text query: a name, a title, or both. | |
| freshness | No | recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data). Trial keys are cached-only and reject realtime with TRIAL_CAP_EXCEEDED; paying upgrades this same key to unlock it. | recent |
| last_name | No | Last-name filter, exact match. | |
| first_name | No | First-name filter, exact match. | |
| max_credits | No | Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling. | |
| past_company | No | Same accepted forms as current_company; names are resolved to an id server-side. | |
| current_company | No | Company name, slug, numeric id, or urn:li:fsd_company URN. Names are fuzzy-matched by LinkedIn; ids and URNs filter exactly. |
| Name | Required | Description |
|---|---|---|
| usage | Yes | |
| common | Yes | |
| entity | Yes | |
| platform | Yes | |
| freshness | Yes | |
| data_as_of | Yes | |
| canonical_url | Yes | |
| schema_version | Yes | |
| platform_fields | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses credit costs, caching behavior (freshness parameter), fuzzy matching on company names, the meaning of is_anonymous in results, and the rejection of certain parameter combinations. Annotations already indicate readOnlyHint and openWorldHint, but the description adds extensive behavioral context beyond these, with no contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is comprehensive and front-loaded with the main purpose, but it is lengthy due to detailed caveats and credit explanations. While every sentence adds value, it could be slightly more concise without losing essential information. Overall well-structured for a complex tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (11 parameters, output schema, annotations, sibling tools), the description covers all important aspects: return value structure, credit costs, pagination behavior, edge cases (is_anonymous, trial keys), and interaction with other tools. It is complete enough for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds crucial meaning: explains that keywords can be derived from name/title filters, clarifies the accepted forms for company filters (name, slug, ID, URN), describes how current_company and past_company are resolved differently, and details the behavior of freshness. This goes well beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: "Find people on LinkedIn by keywords and filters." It further specifies the use case: "The right tool when you have a name, role, or 'who is the X at Y' question without a profile URL." This distinguishes it from siblings like linkedin_get_profile, which is recommended for known persons with a URL.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use and when-not-to-use guidance: for one known person with a URL/slug, call linkedin_get_profile directly; warns against combining past_company NAME or company-URL filter with limit=30; explains when to use numeric IDs for exact filtering. It also details credit costs and pagination trade-offs, helping the agent make efficient choices.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_companyget_postsget_profileget_usage8 fields changedOutput schema / properties / common / properties / free_tierAdded value: +{
+ "anyOf": [
+ {
+ "additionalProperties": false,
+ "properties": {
+ "credits_per_ip_day": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "remaining_today": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "resets": {
+ "description": "UTC midnight when the day budget resets. ISO 8601.",
+ "type": "string"
+ },
+ "used_today": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ }
+ },
+ "required": [
+ "credits_per_ip_day",
+ "used_today",
+ "remaining_today",
+ "resets"
+ ],
+ "type": "object"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "description": "Present on trial keys: the shared per-IP day budget this key actually spends (balance_remaining stays 0 on the free tier; other callers on the same IP draw from the same pool). Null on paid plans."
+}Output schema / properties / common / requiredPrevious value: -[
- "plan",
- "platforms",
- "balance_remaining",
- "realtime_ops_used",
- "realtime_ops_limit",
- "concurrent_limit",
- "recent_receipts",
- "claim_url",
- "upgrade_url",
- "manage_url"
-]New value: +[
+ "plan",
+ "platforms",
+ "balance_remaining",
+ "free_tier",
+ "realtime_ops_used",
+ "realtime_ops_limit",
+ "concurrent_limit",
+ "recent_receipts",
+ "claim_url",
+ "upgrade_url",
+ "manage_url"
+]Output schema / properties / freshness / anyOfPrevious value: -[
- {
- "additionalProperties": false,
- "properties": {
- "cache_age_seconds": {
- "anyOf": [
- {
- "maximum": 9007199254740991,
- "minimum": -9007199254740991,
- "type": "integer"
- },
- {
- "type": "null"
- }
- ]
- },
- "freshness_requested": {
- "enum": [
- "recent",
- "realtime"
- ],
- "type": "string"
- },
- "freshness_served": {
- "enum": [
- "recent",
- "realtime",
- "stale"
- ],
- "type": "string"
- },
- "served_at": {
- "type": "string"
- },
- "source_retrieved_at": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ],
- "description": "When the data was fetched from the source. ISO 8601."
- }
- },
- "required": [
- "source_retrieved_at",
- "served_at",
- "cache_age_seconds",
- "freshness_requested",
- "freshness_served"
- ],
- "type": "object"
- },
- {
- "type": "null"
- }
-]New value: +[
+ {
+ "additionalProperties": false,
+ "properties": {
+ "cache_age_seconds": {
+ "anyOf": [
+ {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ {
+ "type": "null"
+ }
+ ]
+ },
+ "freshness_requested": {
+ "enum": [
+ "recent",
+ "realtime"
+ ],
+ "type": "string"
+ },
+ "freshness_served": {
+ "enum": [
+ "recent",
+ "realtime",
+ "stale"
+ ],
+ "type": "string"
+ },
+ "served_at": {
+ "type": "string"
+ },
+ "served_from_cache": {
+ "description": "True when these bytes were served from cache rather than fetched for this call. source_retrieved_at says how old they are.",
+ "type": "boolean"
+ },
+ "source_retrieved_at": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "description": "When the data was fetched from the source. ISO 8601."
+ }
+ },
+ "required": [
+ "source_retrieved_at",
+ "served_at",
+ "cache_age_seconds",
+ "served_from_cache",
+ "freshness_requested",
+ "freshness_served"
+ ],
+ "type": "object"
+ },
+ {
+ "type": "null"
+ }
+]Output schema / properties / platform / anyOfAdded value: +[
+ {
+ "enum": [
+ "linkedin",
+ "reddit",
+ "x"
+ ],
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+]Output schema / properties / platform / constRemoved value: -"linkedin"Output schema / properties / platform / typeRemoved value: -"string"Output schema / properties / usage / properties / free_tier_hintAdded value: +{
+ "description": "Present on free-tier trial-key calls: what budget the call drew from and that paying upgrades this same key. Near the daily cap on unclaimed keys it also carries a claim link to relay to your human (claiming adds one-time credits).",
+ "type": "string"
+}Output schema / properties / usage / properties / keyless_hintRemoved value: -{
- "description": "Present on keyless calls: what budget the call drew from and how to get a key with its own balance.",
- "type": "string"
-}linkedin_get_companylinkedin_get_postslinkedin_get_profilelinkedin_resolve_urllinkedin_search_peopleresolve_urlsearch_peopleget_company3 fields changedOutput schema / properties / schema_version / constPrevious value: -"1"New value: +"2"Output schema / properties / usage / properties / keyless_hintAdded value: +{
+ "description": "Present on keyless calls: what budget the call drew from and how to get a key with its own balance.",
+ "type": "string"
+}Output schema / properties / usage / properties / provision_hintRemoved value: -{
- "description": "Present on keyless calls: how to get a free trial key with its own budget.",
- "type": "string"
-}get_posts3 fields changedOutput schema / properties / schema_version / constPrevious value: -"1"New value: +"2"Output schema / properties / usage / properties / keyless_hintAdded value: +{
+ "description": "Present on keyless calls: what budget the call drew from and how to get a key with its own balance.",
+ "type": "string"
+}Output schema / properties / usage / properties / provision_hintRemoved value: -{
- "description": "Present on keyless calls: how to get a free trial key with its own budget.",
- "type": "string"
-}get_profile3 fields changedOutput schema / properties / schema_version / constPrevious value: -"1"New value: +"2"Output schema / properties / usage / properties / keyless_hintAdded value: +{
+ "description": "Present on keyless calls: what budget the call drew from and how to get a key with its own balance.",
+ "type": "string"
+}Output schema / properties / usage / properties / provision_hintRemoved value: -{
- "description": "Present on keyless calls: how to get a free trial key with its own budget.",
- "type": "string"
-}get_usage5 fields changedOutput schema / properties / common / properties / platformsAdded value: +{
+ "description": "Platforms this account's key may call. Grants are explicit; write to hello@veezee.io to enable more.",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Output schema / properties / common / requiredPrevious value: -[
- "plan",
- "balance_remaining",
- "realtime_ops_used",
- "realtime_ops_limit",
- "concurrent_limit",
- "recent_receipts",
- "claim_url",
- "upgrade_url",
- "manage_url"
-]New value: +[
+ "plan",
+ "platforms",
+ "balance_remaining",
+ "realtime_ops_used",
+ "realtime_ops_limit",
+ "concurrent_limit",
+ "recent_receipts",
+ "claim_url",
+ "upgrade_url",
+ "manage_url"
+]Output schema / properties / schema_version / constPrevious value: -"1"New value: +"2"Output schema / properties / usage / properties / keyless_hintAdded value: +{
+ "description": "Present on keyless calls: what budget the call drew from and how to get a key with its own balance.",
+ "type": "string"
+}Output schema / properties / usage / properties / provision_hintRemoved value: -{
- "description": "Present on keyless calls: how to get a free trial key with its own budget.",
- "type": "string"
-}provisionresolve_url3 fields changedOutput schema / properties / schema_version / constPrevious value: -"1"New value: +"2"Output schema / properties / usage / properties / keyless_hintAdded value: +{
+ "description": "Present on keyless calls: what budget the call drew from and how to get a key with its own balance.",
+ "type": "string"
+}Output schema / properties / usage / properties / provision_hintRemoved value: -{
- "description": "Present on keyless calls: how to get a free trial key with its own budget.",
- "type": "string"
-}search_people3 fields changedOutput schema / properties / schema_version / constPrevious value: -"1"New value: +"2"Output schema / properties / usage / properties / keyless_hintAdded value: +{
+ "description": "Present on keyless calls: what budget the call drew from and how to get a key with its own balance.",
+ "type": "string"
+}Output schema / properties / usage / properties / provision_hintRemoved value: -{
- "description": "Present on keyless calls: how to get a free trial key with its own budget.",
- "type": "string"
-}get_company8 fields changedInput schema / properties / freshness / defaultRemoved value: -"recent"Input schema / properties / freshness / descriptionRemoved value: -"recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data)."Input schema / properties / freshness / enumRemoved value: -[
- "recent",
- "realtime"
-]Input schema / properties / max_credits / descriptionRemoved value: -"Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling."Input schema / properties / max_credits / maximumRemoved value: -9007199254740991Input schema / properties / max_credits / minimumRemoved value: -0Input schema / properties / max_credits / typePrevious value: -"integer"New value: +"number"Output schema / properties / usage / properties / provision_hintAdded value: +{
+ "description": "Present on keyless calls: how to get a free trial key with its own budget.",
+ "type": "string"
+}get_posts9 fields changedInput schema / properties / cursor / descriptionRemoved value: -"Cursor from a previous page for older posts."Input schema / properties / freshness / defaultRemoved value: -"recent"Input schema / properties / freshness / descriptionRemoved value: -"recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data)."Input schema / properties / freshness / enumRemoved value: -[
- "recent",
- "realtime"
-]Input schema / properties / max_credits / descriptionRemoved value: -"Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling."Input schema / properties / max_credits / maximumRemoved value: -9007199254740991Input schema / properties / max_credits / minimumRemoved value: -0Input schema / properties / max_credits / typePrevious value: -"integer"New value: +"number"Output schema / properties / usage / properties / provision_hintAdded value: +{
+ "description": "Present on keyless calls: how to get a free trial key with its own budget.",
+ "type": "string"
+}get_profile11 fields changedInput schema / properties / freshness / defaultRemoved value: -"recent"Input schema / properties / freshness / descriptionRemoved value: -"recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data)."Input schema / properties / freshness / enumRemoved value: -[
- "recent",
- "realtime"
-]Input schema / properties / max_credits / descriptionRemoved value: -"Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling."Input schema / properties / max_credits / maximumRemoved value: -9007199254740991Input schema / properties / max_credits / minimumRemoved value: -0Input schema / properties / max_credits / typePrevious value: -"integer"New value: +"number"Input schema / properties / sections / descriptionRemoved value: -"Extra profile sections. First 2 are included in the base price."Input schema / properties / sections / items / enumRemoved value: -[
- "about",
- "experience",
- "education",
- "skills"
-]Input schema / properties / sections / maxItemsRemoved value: -4Output schema / properties / usage / properties / provision_hintAdded value: +{
+ "description": "Present on keyless calls: how to get a free trial key with its own budget.",
+ "type": "string"
+}get_usage1 field changedOutput schema / properties / usage / properties / provision_hintAdded value: +{
+ "description": "Present on keyless calls: how to get a free trial key with its own budget.",
+ "type": "string"
+}provision1 field changedOutput schema / properties / usage / properties / provision_hintAdded value: +{
+ "description": "Present on keyless calls: how to get a free trial key with its own budget.",
+ "type": "string"
+}resolve_url1 field changedOutput schema / properties / usage / properties / provision_hintAdded value: +{
+ "description": "Present on keyless calls: how to get a free trial key with its own budget.",
+ "type": "string"
+}search_people21 fields changedInput schema / properties / current_company / descriptionRemoved value: -"Company name, slug, numeric id, or urn:li:fsd_company URN."Input schema / properties / cursor / descriptionRemoved value: -"Cursor from a previous page."Input schema / properties / first_name / descriptionRemoved value: -"First-name filter, exact match."Input schema / properties / freshness / defaultRemoved value: -"recent"Input schema / properties / freshness / descriptionRemoved value: -"recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data)."Input schema / properties / freshness / enumRemoved value: -[
- "recent",
- "realtime"
-]Input schema / properties / keywords / descriptionRemoved value: -"Free-text query: a name, a title, or both."Input schema / properties / last_name / descriptionRemoved value: -"Last-name filter, exact match."Input schema / properties / limit / defaultRemoved value: -10Input schema / properties / limit / descriptionRemoved value: -"How many results to return."Input schema / properties / limit / maximumRemoved value: -30Input schema / properties / limit / minimumRemoved value: -1Input schema / properties / limit / typePrevious value: -"integer"New value: +"number"Input schema / properties / max_credits / descriptionRemoved value: -"Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling."Input schema / properties / max_credits / maximumRemoved value: -9007199254740991Input schema / properties / max_credits / minimumRemoved value: -0Input schema / properties / max_credits / typePrevious value: -"integer"New value: +"number"Input schema / properties / past_company / descriptionRemoved value: -"Same accepted forms as current_company."Input schema / properties / school / descriptionRemoved value: -"School or university name filter."Input schema / properties / title / descriptionRemoved value: -"Current job title filter."Output schema / properties / usage / properties / provision_hintAdded value: +{
+ "description": "Present on keyless calls: how to get a free trial key with its own budget.",
+ "type": "string"
+}get_companyget_postsget_profileget_usageprovisionresolve_urlsearch_peopleClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity – fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge – works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge – works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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