estimate_cost
Estimate API units (free).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | No | ||
| tool | Yes | ||
| count | No |
Tools for Agents
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It only adds that the tool is free, which is a relevant cost trait, but it does not disclose what inputs trigger computation, whether it makes network requests, or what the output represents. This is insufficient for a tool with no structured metadata.
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 extremely short (five words) and front-loaded, with every word earning its place. It is not bloated, but it may be too telegraphic to be fully self-explanatory. As a purely structural score, it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and only a three-parameter schema with zero descriptions, the description must explain the tool's complete behavior, including return values, parameter roles, and edge cases. It only states that it estimates API units for free, leaving the operational contract almost entirely unspecified. The tool's complexity is low but still requires more context to be usable safely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema defines `tool`, `text`, and `count` but provides no descriptions; schema description coverage is 0%. The description does not mention any of these parameters, leaving all semantic meaning to be inferred. Since the description fails to compensate for the low coverage, parameter semantics are essentially undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Estimate API units (free)' uses a clear verb-resource structure, indicating the tool estimates cost in API units. It hints at a cost-related purpose but does not specify for which API or how the estimate is computed, limiting its distinctiveness. None of the sibling tools are named similarly, so it is relatively unique.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not explain when to prefer estimate_cost over sibling tools like `advisor` or `dry_run`, nor does it state any prerequisites or alternatives. The word 'free' implies a cost-related benefit, but not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
C2/5.0
Disambiguation1/5
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 Consistency2/5
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.
Tool Count1/5
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.
Completeness3/5
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.