Skip to main content
Glama

get_provider_operations

Every operation a provider exposes, across all of its OpenAPIs — method, path, operationId, summary, which API it belongs to, and whether it is deprecated. The shortcut for "what can I actually call here?", which otherwise means fetching and parsing every one of their specs. Where the provider publishes agentic-access, each operation also carries its action class and CONSEQUENCE (read vs something that moves money) — filter on those to find the safe surface before letting an agent loose on it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoSubstring match on summary, path, or operationId.
apiNoRestrict to one API, by aid or api slug.
pageNo
pathNoSubstring match on the operation path.
slugYes
limitNo
methodNoRestrict to one HTTP method.
contextNoOptional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable.
consequenceNoAgentic consequence, e.g. "read". Only matches operations with agentic-access profiled.
action_classNoAgentic action class, e.g. "connected".

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that operations may carry agentic-access metadata including CONSEQUENCE (read vs money-moving) and suggests filtering to avoid risky actions. It also notes this saves the work of fetching/parsing every spec, implying an internal aggregation behavior. It does not explicitly state read-only semantics, but the read-oriented framing and lack of write language suggest it's safe.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exactly two sentences, front-loaded with the primary purpose and return fields, then adding the agentic-access nuance and safety advice. Every clause earns its place with no redundancy or padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 10 parameters, 1 required, and no output schema, the description adequately describes the return content (method, path, operationId, summary, API, deprecation) and the filtering rationale. It doesn't mention pagination or sorting, but those are in the schema (page/limit). The description covers the essential context an agent needs to decide whether to call this and how to use the key filters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 70%, so the description partially compensates. It adds meaning to consequence and action_class by explaining their safety implications, and clarifies that api restricts to one API. It also ties the return fields to the purpose, giving context for q and path as substring filters. The description doesn't cover page/limit/context beyond schema, but the core filtering parameters gain clarity.

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?

The description clearly states the tool lists every operation a provider exposes across all OpenAPIs, enumerating fields like method, path, operationId, summary, API membership, and deprecation. It differentiates itself from sibling tools by framing it as the shortcut for 'what can I actually call here?' and explicitly mentions agentic-access details not present in siblings like get_provider_tools or get_provider_schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains the primary use case (discovering callable operations) and provides guidance to filter on consequence/action_class to find the safe surface before agent use. While it doesn't explicitly name alternatives or state when not to use this tool, the phrasing implies it's the go-to for operation discovery, effectively routing around manual spec parsing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.1/5.0
Disambiguation3/5

Most tools are clearly separated by artifact type or resource (find_mcp vs find_openapi vs get_provider vs get_api), but the sheer volume creates some genuinely confusable clusters: apis_io_search vs find_apis vs find_artifacts, and insights_adoption vs insights_dimensions vs find_company_insights. Several readiness-related tools (what_can_i_fix, simulate_fixes, readiness_gates) also share a conceptual boundary, though their descriptions do help.

Naming Consistency3/5

The dominant patterns (find_*, get_*, cohort_*, compare_*) are consistent and predictable, but the set mixes in irregular names like apis_io_search, tag_group_tags, what_can_i_fix, whats_changed, and resolve. These deviations are readable but break the otherwise regular verb_noun convention.

Tool Count2/5

106 tools is far beyond the typical well-scoped server and will impose a heavy selection burden on agents. The server covers a genuinely broad domain (catalog search, ratings, cohorts, agent readiness, lists, exports, feedback), so the count is defensible in scope, but it is still too many to navigate efficiently.

Completeness5/5

The surface is remarkably complete: search and browse, single-entity detail, comparisons, cohort analytics, agent-readiness assessment, saved searches, list management, feedback/correction flows, and full dataset exports are all covered. There are no obvious dead ends, and even minor operations like re-running saved searches or simulating fixes are present.

Resources