list_tools
REST catalog + pricing. Prefer orient then advisor/crp_resolve before picking a tool.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
REST catalog + pricing. Prefer orient then advisor/crp_resolve before picking a tool.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / requiredAdded value: +[]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It indicates that the tool presents a REST catalog and pricing, which is helpful, but it does not state whether the call is read-only, what fields the catalog contains, or whether any external/side effects occur.
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 short and front-loaded, with the most informative part ('REST catalog + pricing') placed first. A small weakness is that 'advisor/crp_resolve' is cryptic and the fragment style depends heavily on the tool name for meaning.
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 no-parameter tool, the purpose is summarized, but since there is no output schema, the description should more explicitly say what the returned catalog contains. The routing sentence helps a selecting agent but does not complete the picture of this tool's output.
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 tool has zero parameters, so there is no schema burden on the description. The description still adds useful context by indicating that the relevant content is REST tooling and pricing.
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?
Name and description together make the resource clear: this tool exposes a REST catalog and pricing information. It is better than a pure restatement of the name, but it is telegraphic and does not explicitly distinguish itself from sibling catalog-like tools such as list_chains.
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 actual routing guidance by recommending orient, then advisor/crp_resolve, before selecting a tool. It names alternatives and an ordering, though it does not state the specific conditions under which list_tools itself is the right choice.
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.
Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.
Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.
With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.
The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.