MCP server for social media and content data including social profiles, engagement metrics, content trends, and influencer analytics for AI agents.
Glama MCP Gateway
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Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.1/5 across 4 of 4 tools scored.
Each tool targets a distinct content platform (Steam games, dev.to articles, Eventbrite events, podcasts) with no overlapping functionality, making it easy for an agent to select the correct tool.
Three tools use 'search_' prefix while one uses 'get_', which is a minor inconsistency. However, all follow a verb_noun pattern and are clear and predictable.
With 4 tools covering diverse content discovery needs, the count is well-scoped and appropriate for a focused social/content server.
The tools cover major content types (games, articles, events, podcasts) but lack common ones like social media posts or videos, leaving moderate gaps for a full social/content scope.
get_steam_gamesASearch Steam game platform for video games by title or keyword. Returns game name, price in USD, average user rating, review count, release date, and Steam store page URL. Use for game discovery, price monitoring, or review research before purchase.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Game title or genre search (e.g. 'Elden Ring', 'strategy games', 'indie puzzle') | |
| max_results | No | Number of game results to return (default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds that it returns specific fields, which is useful but not exhaustive behavioral detail. It does not mention rate limits, pagination, or authentication, but given the annotations, the description is adequate.
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 two sentences: the first explains function and output, the second suggests use cases. It is concise, front-loaded, and contains no fluff. Every sentence adds value.
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 simplicity (2 parameters, no output schema), the description provides sufficient information: what it does, what it returns, and typical use cases. It lacks explicit return structure details, but for a list-based search tool, this is acceptable.
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 description coverage is 100%, and both parameters have clear descriptions in the schema. The tool description does not add any new meaning beyond what is already in the schema. Per baseline for high coverage, a score of 3 is appropriate.
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 verb 'Search' and the specific resource 'Steam game platform for video games'. It lists the returned fields (name, price, rating, etc.) and provides use cases. This differentiates it from sibling tools like search_devto and search_events which target different platforms.
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 gives clear usage context: game discovery, price monitoring, review research. While it does not explicitly state when not to use it or compare to alternatives, the distinct resource (Steam games) and sibling tool names imply that this tool is for Steam-specific searches, leaving little ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_devtoASearch dev.to platform for developer articles, tutorials, and technical posts. Returns article title, author, read time, publication date, tags, and direct link. Use for learning new dev topics, finding tutorials, or staying updated on developer community trends.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keywords for developer content (e.g. 'React tutorial', 'Docker basics', 'TypeScript patterns') | |
| max_results | No | Number of articles to return (default 10, good for recent content) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is clear. The description adds that it returns specific fields (title, author, etc.) but does not discuss pagination, rate limits, or result ordering. Given the annotation coverage, a score of 3 is appropriate.
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 two sentences: the first clearly states the action and output, the second gives usage context. No unnecessary words, and the key information is front-loaded.
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 lists the return fields (title, author, read time, etc.) despite no output schema. It is sufficiently complete for a simple search tool with two parameters. Could include example search terms or mention variation in results, but not necessary.
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?
The input schema covers both parameters (query and max_results) with descriptions, achieving 100% coverage. The description does not add additional meaning beyond the schema, so baseline 3 is appropriate.
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 searches dev.to for developer articles, tutorials, and posts, and lists the specific fields returned (title, author, read time, etc.). It effectively distinguishes itself from siblings like get_steam_games and search_podcasts by specifying the platform and content type.
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 use cases: 'Use for learning new dev topics, finding tutorials, or staying updated on developer community trends.' It does not explicitly mention when not to use or alternatives, but the sibling tools are sufficiently different that no confusion arises.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_eventsASearch Eventbrite for upcoming local and online events by topic and location. Returns event name, date/time, location, ticket price, event description, and registration URL. Use for event discovery, community involvement, or entertainment planning.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Event type or topic to find (e.g. 'tech conference', 'comedy show', 'food festival') | |
| location | No | City or region to search for events (e.g. 'New York, NY', 'Los Angeles', 'virtual') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and openWorldHint=true. Description adds no extra behavioral context beyond the fact that it searches and returns data. No mention of rate limits, auth needs, or edge cases.
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?
Two sentences precisely state the tool's action, scope, and returns. No redundancy or unnecessary words.
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?
No output schema, but description explicitly lists return fields (name, date/time, location, price, description, URL). Annotations cover read-only and open-world aspects. For a search tool, this is adequately complete.
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 parameter descriptions ('query' and 'location'). Description lists return fields but does not add new meaning to parameters beyond schema. Baseline 3 is appropriate.
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?
Description clearly states it searches Eventbrite for events by topic and location, specifying return fields (name, date, location, etc.). Differentiates from sibling tools which target different domains (Steam games, Dev.to, podcasts).
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 'Use for event discovery, community involvement, or entertainment planning.' No explicit when-not-to-use or alternative tools, but siblings are unrelated, making purpose clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_podcastsASearch podcast directories for episodes matching topics or keywords. Returns episode title, podcast name, description, episode length, publish date, and streaming link. Use for podcast discovery, topic research, or building listening playlists.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Podcast topic or search terms (e.g. 'technology news', 'business interviews', 'science explanations') | |
| max_results | No | Number of podcast episodes to retrieve (default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=true, so the description does not need to restate these. It adds value by listing specific return fields and clarifying it is a search operation, enhancing transparency.
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?
Extremely concise: two sentences. First sentence states action and output, second lists use cases. No wasted words.
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 no output schema, the description compensates by listing return fields. With full schema coverage, clear purpose, and sibling context, it is comprehensive for a search tool.
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 both parameters described. The description's mention of 'podcast topic or search terms' matches the schema description, and max_results has a default in schema. No additional semantic value 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 it searches podcast directories for episodes matching topics or keywords, and lists returned fields. It distinguishes itself from siblings (get_steam_games, search_devto, search_events) by specifying 'podcast directories'.
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 use cases ('podcast discovery, topic research, building listening playlists') but does not mention when not to use or alternatives. Since siblings are in different domains, it's clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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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
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