list_pagesList ranked pages tracked for a project — URL, title, company, query count, ownership. Filter by ownership or company domain.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | Yes | ||
| cursor | No | ||
| context | Yes | One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose. | |
| filters | No | ||
| sorting | No | ||
| projectId | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / contextAdded value: +{
+ "description": "One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.",
+ "type": "string"
+}Input schema / requiredPrevious value: -[
- "projectId",
- "limit"
-]New value: +[
+ "projectId",
+ "limit",
+ "context"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description consistently describes a read operation. It adds useful context about ranking and project scoping, but does not disclose pagination behavior, default ordering, or how filters and sorting interact beyond the title.
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 one tight sentence that front-loads the verb and resource and then packs in the key returned fields and filters. Every phrase earns its place and there is no redundant text.
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?
There is no output schema and the tool has nested filter/sorting objects plus a cursor parameter, yet the description omits pagination, default ranking, and the full set of filters. The schema compensates partially, but an agent would still need to infer important behavior around optional sorting and cursor usage.
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 very low at 17%, and the description only explicitly maps ownership and companyDomain filters. Most parameters are self-describing through their names and enums, but cursor, minQueryCount, and sorting semantics are not explained in the description, so it only partially compensates for low schema coverage.
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 uses the specific verb 'List' with a distinct resource ('ranked pages tracked for a project') and enumerates returned fields and filters. This clearly differentiates it from sibling list tools like list_keywords or list_projects.
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 intended context is clear: use this tool to retrieve tracked pages for a project, optionally filtered by ownership or company domain. It does not explicitly name alternatives or state when not to use it, so it falls just short of full 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.
Most tools target a distinct resource and action, and the descriptions carefully cross-reference close alternatives (e.g., add_article_suggestion vs create_article_suggestion_with_input vs accept_idea). A few brief/read variants like get_article_brief, get_write_handoff, and download_brief_markdown could still be confused despite helpful explanations, so the set is not perfectly unambiguous.
Tool names follow a highly consistent verb_noun snake_case pattern throughout: get_*, list_*, create_*, update_*, set_*, add_*, delete_*, start_*, expand_*, etc. Even the less common names like lint_draft and remap_asset are still clear verb_noun constructions.
At 57 tools, this far exceeds the 50+ extreme threshold for a single MCP server. Even for a broad content workflow, the surface is overwhelming and would benefit from consolidation or splitting into focused servers.
The tool set covers the full content lifecycle: project setup, research runs, opportunity clustering, suggestion creation, brief generation, drafting, linting, publishing, reporting, and account management. Minor gaps exist—destinations and projects cannot be deleted via MCP, and there is no direct update for article suggestion metadata—but these are workable via the web UI or existing tools.