Skip to main content
Glama

Jsonplaceholder

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only, open-world, idempotent, and non-destructive. The description adds operational detail: it performs a live page fetch, extracts title/description/key links, and outputs a text blob. It doesn't cover edge cases like unresponsive sites, but the annotation coverage is strong.

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

Conciseness4/5

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

The description is two sentences plus a bullet-like 'Useful for' list. It front-loads the core function and then adds practical use cases. The use-case list could be more compact, but the overall structure is efficient and scannable.

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?

Given only two parameters, strong annotations, and no output schema, the description covers the purpose, the output format, and usage scenarios. The only gap is a brief note about potential network errors, but that is acceptable for a tool with such a simple contract.

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

Parameters3/5

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

The input schema provides full descriptions for both 'url' and 'max_links' (100% coverage), including default and maximum values. The description reinforces the URL's purpose by calling it 'a specific landing page' and explains the overall workflow, but doesn't add new parameter-specific information, so the baseline of 3 is appropriate.

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 opens with a specific verb ('Generate a production-ready llms.txt file for any URL'), names the resource (llms.txt), and explains the underlying mechanics (fetches page, extracts data, emits markdown). It also lists concrete use cases that distinguish it from sibling tools like ai_visibility_check or scan_competitor_ai_presence.

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?

The 'Useful for' section provides three clear scenarios: client indexing, personal projects, and competitor auditing. While it doesn't explicitly name when not to use the tool or compare against alternative tools, the context is sufficient to infer appropriate usage.

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

All Jsonplaceholder toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all serve natural-language question answering. The JSONPlaceholder get_* tools are distinct but introduce an unrelated domain, making tool selection ambiguous in practice.

Naming Consistency3/5

All names use lowercase snake_case, which is a consistent style, but there's no uniform verb_noun pattern. Tools mix action-first names (get_posts, remember, resolve_entity) with noun-oriented names (entity_profile, deep_research, pipeworx_trending). The naming is readable but not highly predictable.

Tool Count2/5

35 tools is far beyond the typical well-scoped range of 3-15. The set includes a full suite of Pipeworx/Polymarket tools plus a separate JSONPlaceholder demo namespace, making the server feel bloated and unfocused. Many meta-tools (discover_tools, suggest_questions, pipeworx_trending) could be consolidated.

Completeness2/5

The server's name and the get_posts/get_post/get_comments/get_users tools suggest a JSONPlaceholder fake API, but CRUD operations are missing: there's no create, update, or delete for posts, comments, or users, and no todos, albums, or photos. The unrelated Pipeworx/Polymarket tools don't address this core domain gap.

Resources