docs-feed-sync
Server Details
Syncs product documentation feeds into MCP resources
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 2 tools
The two tools target clearly different actions and resources: listing feed subscriptions versus fetching update content. An agent can easily decide which to call, and they are complementary rather than overlapping.
Both names follow a consistent verb_noun snake_case pattern (list_doc_feeds, get_doc_updates) with a shared 'doc' domain prefix, making the set predictable and readable.
Two tools is thin for a server ostensibly about feed subscription and synchronization. The count is not wrong, but the surface feels minimal and would likely need expansion to be genuinely useful.
The server is entirely read-only: there is no way to subscribe, unsubscribe, refresh, or trigger a sync despite 'sync' and 'subscribed' in the name and descriptions. Agents would hit dead ends for any feed management workflow.
Available Tools
2 toolsget_doc_updatesCInspect
Fetch documentation updates for a product
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden, and it discloses almost nothing beyond 'fetch' implying a read. It does not say what counts as an 'update' (time window, since-when, version scope), whether results are paginated, or what happens when the product is unknown.
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?
A single short sentence that is front-loaded and free of filler. Its brevity is efficient but tips into under-specification, so it does not fully earn its place relative to the tool's needs.
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?
With no annotations, no output schema, and zero schema description coverage, the description is the only source of behavioral and parameter information, and it supplies neither. An agent would have to guess the input format and the meaning of the returned 'updates.'
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 0%, so the single 'product' parameter is undocumented in the schema. The description only implies it takes a product, without specifying whether it expects a name, slug, or ID, or what a valid value looks like.
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?
States a specific verb and resource: 'Fetch documentation updates for a product.' An agent can tell this retrieves doc updates rather than performing some other doc operation. However, it offers no differentiation from the sibling tool list_doc_feeds, so the boundary between the two must be guessed.
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?
There is no guidance on when to use this tool versus list_doc_feeds, nor any stated preconditions such as whether the product must already be tracked or feeds subscribed. The agent is left to infer the selection rule entirely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_doc_feedsCInspect
List subscribed documentation feeds
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden, yet it discloses nothing about whether pagination exists, what a 'feed' object contains, whether results are scoped to the caller's subscriptions, or how many items are returned by default. The single word 'subscribed' is the only behavioral signal, implying user-scoped results.
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 definition is a single front-loaded phrase with zero filler, which is efficient. It is arguably too terse rather than padded, so it loses a point for under-specification rather than verbosity.
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 one-optional-parameter read tool with no output schema, the description is minimally adequate but leaves the agent guessing about pagination defaults and the shape of a returned feed. Given the low complexity, this is borderline acceptable rather than a serious omission.
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 0%, so the single 'limit' parameter is documented nowhere: the description does not state its purpose, default value, maximum, or whether it paginates. With one undocumented parameter, a score above the baseline is not warranted.
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 gives a specific verb plus resource ('List subscribed documentation feeds'), so an agent knows exactly what collection is returned. It does not, however, distinguish itself from the sibling get_doc_updates, leaving the agent to infer that one lists feeds while the other fetches updates.
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?
There is no when-to-use guidance, no mention of prerequisites (e.g. authentication or an existing subscription), and no reference to the sibling get_doc_updates as an alternative. The only implied usage is 'call this to see feeds', which the name already conveys.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
get_doc_updates - First observed
list_doc_feeds
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceRemote MCP server that lets AI assistants read tracked competitive intelligence — competitor news, reviews, website changes, social posts and ads — and act on it by managing projects, competitors, battle cards, reports and canvases. Every answer carries a source and a date, and write actions only publish when explicitly shared.MIT
- AlicenseCqualityCmaintenanceCompetitor Monitor AI - MCP server providing AI-powered tools and automation by MEOK AI Labs112 npm58 PyPIMIT
- AlicenseAqualityCmaintenanceEnables brand visibility monitoring across major AI platforms like ChatGPT, Claude, Gemini, and Perplexity. It allows users to track visibility scores, analyze competitor data, and receive actionable insights to improve AI-generated brand recommendations.1623 npm1MIT

industrylens-mcpofficial
AlicenseNot gradedqualityBmaintenanceBrowse IndustryLens's published competitive-intelligence reports and head-to-head competitor comparisons from any AI agent — real, source-backed data.MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.