http_fetchFetch a web page and extract its main text content. Useful for reading articles, documentation, and web resources. Returns cleaned text, not raw HTML.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch (http or https only) | |
| max_chars | No | Maximum characters of extracted content to return |
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| title | Yes | ||
| content | Yes | ||
| truncated | Yes | ||
| content_length | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / max_chars / titleAdded value: +"Max Chars"Input schema / properties / url / titleAdded value: +"Url"Input schema / titleAdded value: +"mcp_http_fetchArguments"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals a safe read operation. The description adds useful behavioral context by stating that it 'extracts main text content' and 'returns cleaned text, not raw HTML,' which sets expectations for the output. It does not disclose potential failures like JS-dependent pages or redirects, but the annotation lowers the burden.
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 two short sentences that are front-loaded with the core purpose, then provide a use case and output format. Every sentence adds value and there is no redundancy or padding.
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 simple two-parameter tool with an output schema, a read-only annotation, and a clear description of behavior and output format, the description is complete. The cleaned-text return behavior is explicitly stated, and schema details cover the rest.
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 schema descriptions cover 100% of the parameters, providing details on URL format and max_chars bounds/defaults. The description itself adds no extra parameter semantics beyond the schema's coverage, so baseline 3 is appropriate.
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 uses a specific verb ('Fetch') and resource ('a web page') and clearly states the operation ('extract its main text content'). It also differentiates from sibling tools like web_search by focusing on fetching a given URL rather than searching, and notes the output is cleaned text, not raw HTML.
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 gives clear use cases: 'reading articles, documentation, and web resources.' However, it does not explicitly mention alternatives or when not to use this tool (e.g., 'use web_search when searching for pages'), so it stops short of full exclusion 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 distinct data sources, but several near-duplicates exist: get_token_approvals/get_wallet_approvals, get_defi_positions/get_wallet_positions, and get_wallet_portfolio/get_eth_balance. The descriptions do cross-reference and clarify the differences, so an agent can disambiguate with effort, but names alone are not enough.
The dominant get_<noun> pattern is clear and nearly all names use lowercase snake_case with verb-first conventions. A few tools like calculate, record_predictions, http_fetch, and web_search break the get_ pattern, but the overall style remains predictable.
34 tools is excessive for a single server, even for a broad DeFi/onchain analytics domain. The count is inflated by generic utilities such as calculate, count_text_stats, web_search, and http_fetch, plus multiple overlapping data-retrieval endpoints, making the surface hard to scan.
The set covers an unusually wide range of domain operations: prices, balances, portfolio/positions, approvals, yields, TVL, DEX quotes/volume, transactions, blocks, gas, ENS, contract reads, and risk assessments. It is view-only by design, so missing write/transaction tools is acceptable; minor gaps like address-based token pricing or transaction simulation are workaround-able.