shebanq-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Tools are largely distinct: lookup_feature for metadata, run_mql and run_tf for executing queries, search_bhsa for plain-language queries, and two conversion tools. However, search_bhsa's output sometimes overlaps with run_mql/run_tf when it generates MQL, causing slight potential confusion.
Naming Consistency4/5
Naming follows a consistent verb_noun pattern (lookup_feature, run_mql, run_tf, search_bhsa) with a 'to_' prefix for conversion tools (to_citable_mql, to_tf_template). Minor inconsistency in the naming of search_bhsa (domain name instead of resource) but overall pattern is clear.
Tool Count5/5
6 tools is appropriate for the server's purpose of querying the BHSA Hebrew Bible data. It covers execution, conversion, and metadata lookup without being overwhelming or too sparse.
Completeness4/5
The tool surface covers core workflows: query execution in two formats, conversion between them, plain-language search, and feature metadata. Minor gaps exist (e.g., no tool to list object types or manage sessions), but the set is reasonably complete for read-only querying.
Average 4.3/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 417 commits in the last 12 weeks
- Last stable release on June 23, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the partially describes the return content but does not disclose safety, side effects, or required permissions. It implies a read-only lookup but lacks explicit behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the key purpose. Every word is informative with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (one parameter, no output schema), the description is adequate but minimal. It does not explain the BHSA context, return structure, or how 'gloss and valid values' are represented, leaving the agent to guess.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description does not explain the 'name_or_term' parameter beyond its name. It fails to provide format, examples, or constraints, leaving ambiguity for the agent.
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 clearly states the action ('Look up a BHSA feature') and the expected output ('its gloss and valid values'). It is specific about the resource and distinguishes this tool from siblings like run_mql and search_bhsa, which are for querying or searching.
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?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or limitations. An agent would need to infer usage context from the tool's name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It explains the template syntax and that results are glossed, and mentions the BHSA release pinning. However, it does not explicitly state that the tool is read-only or has no side effects, though the query nature is implied.
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 front-loaded with the main purpose and uses four sentences efficiently. While it could be more structured (e.g., bullet points), it avoids filler and communicates key information without verbosity.
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?
The description lacks details about the return format ('glossed results' is vague), error handling, or prerequisites. Given no output schema, the agent is left guessing about the structure of results. The tool is complex, and the description does not fully compensate for the missing schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'template' has 0% schema description coverage, so the description must add meaning. It provides detailed format: one object per line, indentation for containment, constraints as unquoted feature=value pairs, and that the last line is the target. This is comprehensive and actionable beyond the bare schema.
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 clearly states the tool's verb ('validate and run'), resource ('Text-Fabric search template'), and output ('return glossed results'). It distinguishes itself from siblings by specifying the template syntax and pinning to BHSA 2021, differentiating it from run_mql, lookup_feature, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers some usage hints (e.g., calling lookup_feature to check values) but does not explicitly state when to use this tool versus siblings like run_mql or search_bhsa. Context is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It discloses read-only nature, validation, and glossing, but does not mention error handling, rate limits, or response format. The quoting rules add behavioral context, but gaps remain.
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 concise and front-loaded with purpose. Each sentence adds value, though it could be slightly shorter without losing clarity.
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 the complexity of MQL queries, no output schema, and no annotations, the description covers purpose, constraints, quoting rules, and a referencing sibling. Lacks examples or error behavior, but for a single-parameter tool, it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains the MQL parameter fully, including that it must be a read-only query, quoting rules, and cross-reference to 'lookup_feature' for feature checks. This adds substantial meaning beyond the schema's bare type string.
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 clearly states 'Validate and run a read-only MQL query; return the query and glossed results.' This provides a specific verb ('validate and run'), resource ('MQL query'), and output, distinguishing it from siblings like 'lookup_feature' and 'search_bhsa'.
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?
It specifies that only read-only queries (SELECT/GET) are accepted and provides quoting rules. It also mentions 'lookup_feature' as a way to check feature kinds, implying when to use an alternative. However, it does not explicitly state when not to use this tool or provide direct comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses determinism, no model involvement, transformation rules (brackets to indentation, AND to space, quoted strings lose quotes), and handling of GET clauses and refused operators. More detail on error handling could improve it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (4 sentences), front-loaded with the core purpose, and each sentence adds unique value. No wasted words.
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?
The description covers purpose, behavior, limitations, and sibling relationship. For a conversion tool with one parameter and no output schema, it is largely complete. Could mention output format explicitly.
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 description adds meaning to the single parameter 'mql' by specifying it is an 'Emdros MQL query', but does not elaborate on expected format or syntax. Given 0% schema coverage, this provides necessary context but could be more precise.
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 clearly states the tool's purpose: converting an Emdros MQL query to a Text-Fabric search template. It also differentiates from siblings by noting it is the mirror of to_citable_mql, and its behavior contrasts with run_mql and run_tf.
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 description implies when to use (for conversion) and specifies limitations (refuses OR, NOT, FOCUS, sequence operators). However, it does not explicitly state when not to use or provide alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses deterministic behavior: 'indentation becomes brackets, spaces become AND, string features get quoted from the catalogue.' Also explains refusal of complex templates with explanation.
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?
Two paragraphs, clear and front-loaded. First sentence defines purpose, second adds details. No wasted words, though 'Deterministic, no model involved' is somewhat unnecessary. Efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter, no output schema, and moderate complexity (language conversion), the description covers behavior, limitations, alternatives, and transformation rules. Complete for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'template' has no schema description (0% coverage). The description explains it is a Text-Fabric search template and gives transformation rules, adding meaning beyond the schema. Lacks explicit syntax examples but sufficient for use.
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 clearly states the tool converts a Text-Fabric search template to MQL for SHEBANQ, with a specific verb 'Convert' and resource. It explicitly distinguishes itself as the mirror of 'to_tf_template', differentiating from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use case: 'Use this to turn a notebook query into a citable SHEBANQ saved query.' Also states when not to use: 'Templates using TF constructs beyond ... are refused with an explanation.' Mentions the alternative tool 'to_tf_template' as the mirror.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses behavioral traits: returns generated MQL+results or a primer depending on deployment, and includes Text-Fabric equivalent. No annotations, but description adequately covers behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, front-loaded with primary function, no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Completes the picture for a simple tool with one parameter: explains output modes, deployment differences, and related tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single 'question' parameter is described as 'plain-language question about the Hebrew Bible', adding semantic context beyond the schema alone. Could include an example, but sufficient.
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?
Clearly states it answers plain-language questions about the Hebrew Bible. Differentiates from siblings like run_mql by explaining it can generate MQL or provide a primer.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes two usage modes (with server-side LLM vs. public deploy) and directs to alternatives like write-mql prompt or run_mql for raw queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![shebanq-mcp MCP server](https://glama.ai/mcp/servers/Jossifresben/shebanq-mcp/badges/card.svg)](https://glama.ai/mcp/servers/Jossifresben/shebanq-mcp)

Score Badge

Copy to your README.md:

[![shebanq-mcp MCP server](https://glama.ai/mcp/servers/Jossifresben/shebanq-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Jossifresben/shebanq-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Jossifresben/shebanq-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server