search_instagramOrganic Instagram REELS keyword search (/v2/instagram/reels/search — our only IG keyword surface; profile/hashtag pulls go through fetch_social_data with a handle). Returns compact JSON {desc, author, handle, plays, likes, link, cover} per reel, ranked by plays. Spends about a credit.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max reels returned (1–25, default 8) | |
| query | Yes | keyword to search reels for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover safety (readOnlyHint, destructiveHint), so the description adds beyond them: it specifies the return format (compact JSON with listed fields), ranking by plays, and a cost signal ('Spends about a credit'). This gives the agent a concrete expectation of output and resource usage. It doesn't contradict 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?
Two sentences, each packed with value: scope and endpoint in the first, output format and cost in the second. No filler, and the most important distinguishing info (only IG keyword surface) is front-loaded. Excellent conciseness.
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?
For a search tool with no output schema, the description fully specifies the return structure (fields), ranking, and cost. It also clarifies scope vs alternatives. Nothing essential for an agent to call it correctly is missing. The mention of 'about a credit' helps with cost estimation, which is rare and useful.
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% and both parameters (query and limit) have descriptions in the schema, so baseline is 3. The description adds no extra parameter details beyond what the schema already provides; it only re-states the purpose. It doesn't hurt, but it doesn't add value either.
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 states a specific verb ('search'), a resource ('Instagram REELS'), and scope ('keyword search'), and clearly distinguishes it from profile/hashtag pulls by naming fetch_social_data as the alternative. It also identifies itself as the only IG keyword surface, which sets it apart from search_instagram_hashtag and search_instagram_shopping_products.
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 tells the agent when to use this tool: for keyword search of reels, and when not to (profile/hashtag pulls go through fetch_social_data). It also notes the output is ranked by plays, giving a hint about result ordering. This is clear routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With 293 tools, the surface is enormous and many tools have overlapping purposes—multiple posting tools (post_to_meta, post_to_linkedin, schedule_post, etc.), multiple analytics tools per channel, and several search tools (search_meta_ads, search_instagram, search_reddit...). While each description is detailed, the volume makes it difficult for an agent to reliably distinguish between similar tools without careful reading, leading to frequent misselection.
The naming is largely consistent with a verb_noun pattern (post_to_*, list_*, create_*, delete_*, update_*, manage_*). There are clear families for major operations. A few outliers like 'google_business_account', 'hermoso_capabilities', and 'store_get' break the pattern, but the overwhelming majority follow a predictable structure, making navigation somewhat easier.
293 tools is far beyond any reasonable scope for a single MCP server, even for a comprehensive marketing platform. The calibration guide flags 50+ as an extreme mismatch, and this is nearly six times that threshold. Such a large surface overwhelms context windows, increases the probability of misselection, and makes it impractical for agents to learn or use effectively.
The tool set covers a vast domain: ad creation and rendering, posting across nine+ social channels, analytics and reporting, file management (Drive/OneDrive), competitor research, brand management, and more. It appears to provide CRUD and lifecycle coverage for most resources. While there may be minor gaps given the immense scope, the overall coverage is impressively comprehensive.